Poolside Laguna S 2.1 is a mixture-of-experts language model designed for agentic coding and long-horizon work. It has 118B total parameters and about 8B active per token, placing it between the smaller Laguna XS 2.1 and larger Laguna M.1 in the manufacturer’s series. The model processes text and produces text, code, reasoning and tool-call requests in a tool-connected application.
Its architecture combines global and sliding-window attention across 48 layers: 12 global layers and 36 sliding-window layers. The 512-token sliding window describes local attention within those layers, while the documented model context is 1,048,576 tokens. These are different measurements. The larger context supports tasks that accumulate source files, observations and decisions over many steps. Keep the working objective and meaningful evidence together, and reserve space for subsequent reasoning and the final response within the applicable configuration.
The model’s central purpose is software work that requires persistence, checking and revision. For a repository task, provide the expected behavior, relevant files as text, dependency information and the actual failing output. Ask for a focused result: “Trace the cause of this regression, propose a change that preserves the documented interface, and identify the checks that would distinguish a fix from an unrelated change.” In a tool-connected workflow, give the model the result of each executed check before it chooses the next step.
For optimization work, provide measurements and an explicit objective. An example is: “Use this profile and allocation report to identify a bottleneck. Propose one change, explain the expected effect, and compare it with the baseline after the same measurement is repeated.” This makes a performance claim inspectable. Poolside’s release discusses verification and continued progress as important behaviors, but a persuasive explanation alone does not establish that a revision is faster, correct or complete. Preserve the observations that support keeping or rejecting the change.
Laguna S 2.1 supports interleaved thinking before and between tool calls. The manufacturer recommends preserving reasoning content from earlier assistant messages in subsequent history; dropping those blocks can cause reasoning to stop in follow-up steps. Keep the assistant response, tool-call information and the corresponding tool result together. This helps a longer task continue from its actual state rather than repeatedly reconstructing what was already attempted.
The documented thinking choices are off and max, with max enabled by default in the maker’s described setup. The model determines its thinking budget in that mode; the release does not present separate low, medium and high effort controls. For agentic coding, Poolside recommends enabling thinking and retaining it between steps. State the final artifact and completion criteria explicitly, and distinguish a long reasoning sequence from the completed result. The manufacturer identifies overthinking, especially on competition mathematics, as a substantive limitation.
Tool definitions need particular care. Poolside reports that S 2.1 can initially rely on a familiar tool interface instead of closely following a similar but different schema. It can also produce malformed or incorrectly escaped JSON when a tool argument contains a nested array. Give the actual argument definitions, validate proposed calls, and return a precise error when the call does not match. The maker describes correction through in-context feedback as a way to resolve some of these mismatches. Keep the resulting tool observation available for the next decision.
The manufacturer publishes separate BF16 and quantized variants and an optional DFlash draft model. Those are distinct artifacts and serving recipes, so a quantized checkpoint’s memory use or speculative-decoding performance should not be assumed from the general model name. For ordinary text work, prepare source code, command output and document extracts as readable text. The release notes the model’s lack of vision capability; a screenshot needs a textual observation or an appropriate external inspection step before it can support the analysis.
Request a final deliverable that identifies the changed artifact, the important decisions, the observed checks and any unresolved condition. For a code review, connect findings to the relevant source and show why each issue affects behavior. For a repair, separate the proposed patch from a patch whose behavior was actually checked. Retaining reasoning and tool history helps the model work through the task, while the final artifact and recorded results establish what was accomplished.
", "aboutText": "Poolside Laguna S 2.1 is a mixture-of-experts language model designed for agentic coding and long-horizon work. It has 118B total parameters and about 8B active per token, placing it between the smaller Laguna XS 2.1 and larger Laguna M.1 in the manufacturer’s series. The model processes text and produces text, code, reasoning and tool-call requests in a tool-connected application.\n\nIts architecture combines global and sliding-window attention across 48 layers: 12 global layers and 36 sliding-window layers. The 512-token sliding window describes local attention within those layers, while the documented model context is 1,048,576 tokens. These are different measurements. The larger context supports tasks that accumulate source files, observations and decisions over many steps. Keep the working objective and meaningful evidence together, and reserve space for subsequent reasoning and the final response within the applicable configuration.\n\nThe model’s central purpose is software work that requires persistence, checking and revision. For a repository task, provide the expected behavior, relevant files as text, dependency information and the actual failing output. Ask for a focused result: “Trace the cause of this regression, propose a change that preserves the documented interface, and identify the checks that would distinguish a fix from an unrelated change.” In a tool-connected workflow, give the model the result of each executed check before it chooses the next step.\n\nFor optimization work, provide measurements and an explicit objective. An example is: “Use this profile and allocation report to identify a bottleneck. Propose one change, explain the expected effect, and compare it with the baseline after the same measurement is repeated.” This makes a performance claim inspectable. Poolside’s release discusses verification and continued progress as important behaviors, but a persuasive explanation alone does not establish that a revision is faster, correct or complete. Preserve the observations that support keeping or rejecting the change.\n\nLaguna S 2.1 supports interleaved thinking before and between tool calls. The manufacturer recommends preserving reasoning content from earlier assistant messages in subsequent history; dropping those blocks can cause reasoning to stop in follow-up steps. Keep the assistant response, tool-call information and the corresponding tool result together. This helps a longer task continue from its actual state rather than repeatedly reconstructing what was already attempted.\n\nThe documented thinking choices are off and max, with max enabled by default in the maker’s described setup. The model determines its thinking budget in that mode; the release does not present separate low, medium and high effort controls. For agentic coding, Poolside recommends enabling thinking and retaining it between steps. State the final artifact and completion criteria explicitly, and distinguish a long reasoning sequence from the completed result. The manufacturer identifies overthinking, especially on competition mathematics, as a substantive limitation.\n\nTool definitions need particular care. Poolside reports that S 2.1 can initially rely on a familiar tool interface instead of closely following a similar but different schema. It can also produce malformed or incorrectly escaped JSON when a tool argument contains a nested array. Give the actual argument definitions, validate proposed calls, and return a precise error when the call does not match. The maker describes correction through in-context feedback as a way to resolve some of these mismatches. Keep the resulting tool observation available for the next decision.\n\nThe manufacturer publishes separate BF16 and quantized variants and an optional DFlash draft model. Those are distinct artifacts and serving recipes, so a quantized checkpoint’s memory use or speculative-decoding performance should not be assumed from the general model name. For ordinary text work, prepare source code, command output and document extracts as readable text. The release notes the model’s lack of vision capability; a screenshot needs a textual observation or an appropriate external inspection step before it can support the analysis.\n\nRequest a final deliverable that identifies the changed artifact, the important decisions, the observed checks and any unresolved condition. For a code review, connect findings to the relevant source and show why each issue affects behavior. For a repair, separate the proposed patch from a patch whose behavior was actually checked. Retaining reasoning and tool history helps the model work through the task, while the final artifact and recorded results establish what was accomplished.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
Propose a measured loop optimization\n\nReview this Python loop: totals = {}\nfor row in rows:\n totals[row["id"]] = sum(x["amount"] for x in rows if x["id"] == row["id"])\n It repeatedly rescans rows. Propose a single-pass replacement preserving per-ID totals. Give a duplicate-ID example and explain how to compare results and timing on the same input without claiming a speed measurement.A single-pass aggregation patch, correctness example and a baseline comparison procedure.Check repeated IDs accumulate all amounts and the proposal contains no fabricated timing improvement.poolside/laguna-s-2.1Poolside Laguna S 2.1 is a text model for agentic coding and long-horizon work, with 118B total and about 8B active parameters. It combines a 1,048,576-token model context with native tool use and interleaved reasoning; preserving prior thinking helps it continue effectively across tool calls.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Poolside Laguna S 2.1 is a mixture-of-experts language model designed for agentic coding and long-horizon work. It has 118B total parameters and about 8B active per token, placing it between the smaller Laguna XS 2.1 and larger Laguna M.1 in the manufacturer’s series. The model processes text and produces text, code, reasoning and tool-call requests in a tool-connected application.
Its architecture combines global and sliding-window attention across 48 layers: 12 global layers and 36 sliding-window layers. The 512-token sliding window describes local attention within those layers, while the documented model context is 1,048,576 tokens. These are different measurements. The larger context supports tasks that accumulate source files, observations and decisions over many steps. Keep the working objective and meaningful evidence together, and reserve space for subsequent reasoning and the final response within the applicable configuration.
The model’s central purpose is software work that requires persistence, checking and revision. For a repository task, provide the expected behavior, relevant files as text, dependency information and the actual failing output. Ask for a focused result: “Trace the cause of this regression, propose a change that preserves the documented interface, and identify the checks that would distinguish a fix from an unrelated change.” In a tool-connected workflow, give the model the result of each executed check before it chooses the next step.
For optimization work, provide measurements and an explicit objective. An example is: “Use this profile and allocation report to identify a bottleneck. Propose one change, explain the expected effect, and compare it with the baseline after the same measurement is repeated.” This makes a performance claim inspectable. Poolside’s release discusses verification and continued progress as important behaviors, but a persuasive explanation alone does not establish that a revision is faster, correct or complete. Preserve the observations that support keeping or rejecting the change.
Laguna S 2.1 supports interleaved thinking before and between tool calls. The manufacturer recommends preserving reasoning content from earlier assistant messages in subsequent history; dropping those blocks can cause reasoning to stop in follow-up steps. Keep the assistant response, tool-call information and the corresponding tool result together. This helps a longer task continue from its actual state rather than repeatedly reconstructing what was already attempted.
The documented thinking choices are off and max, with max enabled by default in the maker’s described setup. The model determines its thinking budget in that mode; the release does not present separate low, medium and high effort controls. For agentic coding, Poolside recommends enabling thinking and retaining it between steps. State the final artifact and completion criteria explicitly, and distinguish a long reasoning sequence from the completed result. The manufacturer identifies overthinking, especially on competition mathematics, as a substantive limitation.
Tool definitions need particular care. Poolside reports that S 2.1 can initially rely on a familiar tool interface instead of closely following a similar but different schema. It can also produce malformed or incorrectly escaped JSON when a tool argument contains a nested array. Give the actual argument definitions, validate proposed calls, and return a precise error when the call does not match. The maker describes correction through in-context feedback as a way to resolve some of these mismatches. Keep the resulting tool observation available for the next decision.
The manufacturer publishes separate BF16 and quantized variants and an optional DFlash draft model. Those are distinct artifacts and serving recipes, so a quantized checkpoint’s memory use or speculative-decoding performance should not be assumed from the general model name. For ordinary text work, prepare source code, command output and document extracts as readable text. The release notes the model’s lack of vision capability; a screenshot needs a textual observation or an appropriate external inspection step before it can support the analysis.
Request a final deliverable that identifies the changed artifact, the important decisions, the observed checks and any unresolved condition. For a code review, connect findings to the relevant source and show why each issue affects behavior. For a repair, separate the proposed patch from a patch whose behavior was actually checked. Retaining reasoning and tool history helps the model work through the task, while the final artifact and recorded results establish what was accomplished.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits poolside/laguna-s-2.1 | Default— |
Fieldmessages | Typemessage[] | RequirementRequired | Values & limits Minimum items: 1 | Default— |
Fieldmessages[].role | Typestring | RequirementRequired | Values & limits Examples: system, user, assistant, tool, function Conditions: These roles are explicitly represented by Source compatibility types; the exact model role set remains model-specific. Model-specific | Default— |
Fieldmessages[].content | Typestring|content_part[]|null | RequirementOptional | Values & limits | Default— |
Fieldmessages[].tool_calls | Typetool_call[] | RequirementOptional | Values & limits | Default— |
Fieldmessages[].tool_call_id | Typestring | RequirementOptional | Values & limits Applies when: role=tool; use actual returned ID | Default— |
Fieldmessages[].name | Typestring | RequirementOptional | Values & limits | Default— |
Fieldmessages[].reasoning_content | Typestring | RequirementOptional | Values & limits Applies when: Round-trip actual returned reasoning where this model uses it | Default— |
Fieldmessages[].reasoning_details | Typeobject|array | RequirementOptional | Values & limits Model-specific | Default— |
Fieldmax_tokens | Typeinteger | RequirementOptional | Values & limits Minimum: 1 Model-specific | Default— |
Fieldmax_completion_tokens | Typeinteger | RequirementOptional | Values & limits Minimum: 1 Model-specific | Default— |
Fieldstream | Typeboolean | RequirementOptional | Values & limits | Defaultfalse |
Fieldstream_options.include_usage | Typeboolean | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtemperature | Typenumber | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtop_p | Typenumber | RequirementOptional | Values & limits Model-specific | Default— |
Fieldstop | Typestring|string[] | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtools | Typefunction_tool[] | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtools[].type | Typestring | RequirementOptional | Values & limits function Model-specific | Default— |
Fieldtools[].function.name | Typestring | RequirementRequired | Values & limits Applies when: function tool supplied | Default— |
Fieldtools[].function.description | Typestring | RequirementOptional | Values & limits | Default— |
Fieldtools[].function.parameters | TypeJSON_schema | RequirementOptional | Values & limits | Default— |
Fieldtool_choice | Typestring|object | RequirementOptional | Values & limits Model-specific | Default— |
Fieldparallel_tool_calls | Typeboolean | RequirementOptional | Values & limits Model-specific | Default— |
Fieldresponse_format | Typeobject | RequirementOptional | Values & limits Model-specific | Default— |
Fieldreasoning_effort | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldreasoning | Typeobject | RequirementOptional | Values & limits Model-specific | Default— |
Fieldservice_tier | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldinstructions | Typestring | RequirementOptional | Values & limits Compatibility field | Default— |
Fieldfunctions | Typefunction[] | RequirementOptional | Values & limits Model-specific | Default— |
Fieldfunction_call | Typestring|object | RequirementOptional | Values & limits Model-specific | Default— |
Fieldprompt_cache_options | Typeobject | RequirementOptional | Values & limits Model-specific | Default— |
Fieldmodalities | Typestring[] | RequirementOptional | Values & limits text | Default— |
Fieldprovider | Typeobject | RequirementOptional | Values & limits Conditions: provider.options is rejected on this Ultra text route | Default— |
Fieldcache_control.ttl | Typestring | RequirementOptional | Values & limits Conditions: 1h is rejected in actual prompt-cache locations | Default— |
Text · UTF8 text
messages[].content
| Parameter | Contract |
|---|---|
Parametercontent_type | Contractapplication/json |
Parameterrequired | Contractmodel, messages |
Parametermaterial_types | Contracttext |
Parametermessages | Contractmessage array with role/content shape |
Parameterfile_material | Contractactual UTF-8 document inserted as literal text; no file ID inferred |
Parameteroptional_controls | Contractstream, temperature, top_p, stop, max_tokens, max_completion_tokens, reasoning, response_format, tools, tool_choice |
Parametercontrol_scope | Contractmodel-specific; user function tools differ from hosted tools |
Parameteroutput_modalities | Contracttext |
Parameterhosted_operations | Contractfalse |
Parametermultimodal_input_binding | Contractnull |
Lifecycle: synchronous_or_streamed
Send exact model ID with messages.
Context length is not an output cap.
A text file may supply literal text; image/audio/file-object inputs need exact binding.
Tariff references do not add request options. Supported parameters are listed for the selected method.
Match each charge to the conditions shown. Use one rate for each set of alternative configurations.
| Configuration | Rate schedule | Context tokens, more than | Context tokens, up to and including | Charge |
|---|---|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Context tokens, more than— | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.0002 / 1K tokens Output · Output tokens $0.0004 / 1K tokens Input · Cache read $0.00002 / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
| ConfigurationRequest-length tier 1 | Rate schedule— | Context tokens, more than0 | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.0002 / 1K tokens Output · Output tokens $0.0004 / 1K tokens Input · Cache read $0.00002 / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "poolside/laguna-s-2.1", "messages": [{"role": "user", "content": "Explain the difference between an observation and a hypothesis in three sentences."}]}')\n\nPath("request.json").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/chat/completions \\\n -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\nr=json.loads(Path("result.json").read_text())\nfor x in r.get("choices",[]):\n if x.get("message",{}).get("content"): print(x["message"]["content"])\nPY_RESULT\nfrom openai import OpenAI\nimport os\n\nif not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")\nclient=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0)\nresult=client.chat.completions.create(model='poolside/laguna-s-2.1',messages=[{"role":"user","content":"Explain observations and hypotheses in three sentences."}])\nimport base64, json\nfrom pathlib import Path\n\ndef save_result(data, stem="result"):\n # Keep every field, including usage, IDs, reasoning and tool requests.\n Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")\n print(json.dumps(data, ensure_ascii=False, indent=2))\n def save_blob(encoded, suffix, label):\n raw = base64.b64decode(encoded, altchars=b"-_", validate=True)\n ext = "png" if raw.startswith(b"\\x89PNG\\r\\n\\x1a\\n") else "jpg" if raw.startswith(b"\\xff\\xd8") else "webp" if raw[:4] == b"RIFF" and raw[8:12] == b"WEBP" else suffix\n file = Path(stem + "-" + label + "." + ext)\n file.write_bytes(raw)\n print("Saved", file)\n for index, block in enumerate(data.get("content") or []):\n print("Content", index, json.dumps(block, ensure_ascii=False))\n for choice in data.get("choices") or []:\n print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False))\n for index, item in enumerate(data.get("output") or []):\n print("Output", index, json.dumps(item, ensure_ascii=False))\n if item.get("type") == "image_generation_call" and item.get("result"):\n save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index))\n for ci, candidate in enumerate(data.get("candidates") or []):\n for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []):\n print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False))\n blob = part.get("inlineData") or part.get("inline_data")\n if blob and blob.get("data"):\n mime = blob.get("mimeType") or blob.get("mime_type")\n suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin")\n save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi))\n\nsave_result(result.to_dict(mode="json"))\nfrom openai import OpenAI\nimport os\nfrom pathlib import Path\n\nif not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")\nclient=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0)\nimport base64, json\nfrom pathlib import Path\n\ndef save_result(data, stem="result"):\n # Keep every field, including usage, IDs, reasoning and tool requests.\n Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")\n print(json.dumps(data, ensure_ascii=False, indent=2))\n def save_blob(encoded, suffix, label):\n raw = base64.b64decode(encoded, altchars=b"-_", validate=True)\n ext = "png" if raw.startswith(b"\\x89PNG\\r\\n\\x1a\\n") else "jpg" if raw.startswith(b"\\xff\\xd8") else "webp" if raw[:4] == b"RIFF" and raw[8:12] == b"WEBP" else suffix\n file = Path(stem + "-" + label + "." + ext)\n file.write_bytes(raw)\n print("Saved", file)\n for index, block in enumerate(data.get("content") or []):\n print("Content", index, json.dumps(block, ensure_ascii=False))\n for choice in data.get("choices") or []:\n print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False))\n for index, item in enumerate(data.get("output") or []):\n print("Output", index, json.dumps(item, ensure_ascii=False))\n if item.get("type") == "image_generation_call" and item.get("result"):\n save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index))\n for ci, candidate in enumerate(data.get("candidates") or []):\n for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []):\n print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False))\n blob = part.get("inlineData") or part.get("inline_data")\n if blob and blob.get("data"):\n mime = blob.get("mimeType") or blob.get("mime_type")\n suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin")\n save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi))\n\nwith client.chat.completions.stream(model='poolside/laguna-s-2.1',messages=[{"role":"user","content":"Explain observations and hypotheses in three sentences."}]) as stream:\n with Path("stream-events.jsonl").open("w",encoding="utf-8") as log:\n for event in stream:\n payload=event.to_dict(mode="json")\n log.write(json.dumps(payload,ensure_ascii=False)+"\\n"); log.flush()\n print(json.dumps(payload,ensure_ascii=False))\n final=stream.get_final_completion()\n save_result(final.to_dict(mode="json"),"stream-result")\njson or SSE
Keep every complete returned response, file and content part.
Keep every choice and its content/tool calls/reasoning. In SSE assemble deltas by choice/tool index, retain the final finish reason and usage.
Keep request/response IDs and headers. No generic text job retrieval URL is supplied.
| Field | Type |
|---|---|
Fieldchoices[].index | Typeinteger |
Fieldchoices[].message.content | Typestring|null |
Fieldchoices[].message.tool_calls | Typetool_call[] |
Fieldchoices[].message.reasoning_content | Typestring |
Fieldchoices[].message.reasoning_details | Typeobject|array |
Fieldchoices[].finish_reason | Typestring|null |
Fieldusage | Typeobject |
| Response | Condition | Next step |
|---|---|---|
Response400 · invalid_request_error | ConditionInvalid required fields, material representation or parameter value | Next stepCorrect the indicated fields before a new creation request. |
Ailoot Ultra poolside/laguna-s-2.1
Use the method paths shown above.
poolside/laguna-s-2.1Poolside Laguna S 2.1 is a text model for agentic coding and long-horizon work, with 118B total and about 8B active parameters. It combines a 1,048,576-token model context with native tool use and interleaved reasoning; preserving prior thinking helps it continue effectively across tool calls.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Poolside Laguna S 2.1 is a mixture-of-experts language model designed for agentic coding and long-horizon work. It has 118B total parameters and about 8B active per token, placing it between the smaller Laguna XS 2.1 and larger Laguna M.1 in the manufacturer’s series. The model processes text and produces text, code, reasoning and tool-call requests in a tool-connected application.
Its architecture combines global and sliding-window attention across 48 layers: 12 global layers and 36 sliding-window layers. The 512-token sliding window describes local attention within those layers, while the documented model context is 1,048,576 tokens. These are different measurements. The larger context supports tasks that accumulate source files, observations and decisions over many steps. Keep the working objective and meaningful evidence together, and reserve space for subsequent reasoning and the final response within the applicable configuration.
The model’s central purpose is software work that requires persistence, checking and revision. For a repository task, provide the expected behavior, relevant files as text, dependency information and the actual failing output. Ask for a focused result: “Trace the cause of this regression, propose a change that preserves the documented interface, and identify the checks that would distinguish a fix from an unrelated change.” In a tool-connected workflow, give the model the result of each executed check before it chooses the next step.
For optimization work, provide measurements and an explicit objective. An example is: “Use this profile and allocation report to identify a bottleneck. Propose one change, explain the expected effect, and compare it with the baseline after the same measurement is repeated.” This makes a performance claim inspectable. Poolside’s release discusses verification and continued progress as important behaviors, but a persuasive explanation alone does not establish that a revision is faster, correct or complete. Preserve the observations that support keeping or rejecting the change.
Laguna S 2.1 supports interleaved thinking before and between tool calls. The manufacturer recommends preserving reasoning content from earlier assistant messages in subsequent history; dropping those blocks can cause reasoning to stop in follow-up steps. Keep the assistant response, tool-call information and the corresponding tool result together. This helps a longer task continue from its actual state rather than repeatedly reconstructing what was already attempted.
The documented thinking choices are off and max, with max enabled by default in the maker’s described setup. The model determines its thinking budget in that mode; the release does not present separate low, medium and high effort controls. For agentic coding, Poolside recommends enabling thinking and retaining it between steps. State the final artifact and completion criteria explicitly, and distinguish a long reasoning sequence from the completed result. The manufacturer identifies overthinking, especially on competition mathematics, as a substantive limitation.
Tool definitions need particular care. Poolside reports that S 2.1 can initially rely on a familiar tool interface instead of closely following a similar but different schema. It can also produce malformed or incorrectly escaped JSON when a tool argument contains a nested array. Give the actual argument definitions, validate proposed calls, and return a precise error when the call does not match. The maker describes correction through in-context feedback as a way to resolve some of these mismatches. Keep the resulting tool observation available for the next decision.
The manufacturer publishes separate BF16 and quantized variants and an optional DFlash draft model. Those are distinct artifacts and serving recipes, so a quantized checkpoint’s memory use or speculative-decoding performance should not be assumed from the general model name. For ordinary text work, prepare source code, command output and document extracts as readable text. The release notes the model’s lack of vision capability; a screenshot needs a textual observation or an appropriate external inspection step before it can support the analysis.
Request a final deliverable that identifies the changed artifact, the important decisions, the observed checks and any unresolved condition. For a code review, connect findings to the relevant source and show why each issue affects behavior. For a repair, separate the proposed patch from a patch whose behavior was actually checked. Retaining reasoning and tool history helps the model work through the task, while the final artifact and recorded results establish what was accomplished.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits poolside/laguna-s-2.1 | Default— |
Fieldmessages | Typemessage[] | RequirementRequired | Values & limits | Default— |
Fieldmessages[].role | Typestring | RequirementRequired | Values & limits user, assistant | Default— |
Fieldmessages[].content | Typestring | RequirementRequired | Values & limits | Default— |
Fieldmax_tokens | Typeinteger | RequirementRequired | Values & limits | Default— |
Fieldstream | Typeboolean | RequirementOptional | Values & limits | Defaultfalse |
Text · UTF-8 text
messages[].content string
| Parameter | Contract |
|---|---|
Parametercontent_type | Contractapplication/json |
Parameterrequired | Contractmodel, messages, max_tokens |
Parameteroptional_controls | Contractstream |
Parametermaterial_types | Contracttext |
Parametertext_file_material | ContractRead the actual UTF-8 document and put its text in the indicated input or message content string. |
Lifecycle: synchronous_or_streamed
Tariff references do not add request options. Supported parameters are listed for the selected method.
Match each charge to the conditions shown. Use one rate for each set of alternative configurations.
| Configuration | Rate schedule | Context tokens, more than | Context tokens, up to and including | Charge |
|---|---|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Context tokens, more than— | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.0002 / 1K tokens Output · Output tokens $0.0004 / 1K tokens Input · Cache read $0.00002 / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
| ConfigurationRequest-length tier 1 | Rate schedule— | Context tokens, more than0 | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.0002 / 1K tokens Output · Output tokens $0.0004 / 1K tokens Input · Cache read $0.00002 / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "poolside/laguna-s-2.1",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\n: "${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}"\nexport AILOOT_TEXT_FILE\npython3 - <<'PY_REQUEST'\nimport json, os\nfrom pathlib import Path\n\nsource_file = Path(os.environ['AILOOT_TEXT_FILE'])\nif not source_file.is_file():\n raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')\ntext = source_file.read_text(encoding='utf-8')\nif not text.strip():\n raise SystemExit('Choose a document containing text')\npayload = {'model': 'poolside/laguna-s-2.1', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]}\npayload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\\n\\n' + text\nPath('request.json').write_text(json.dumps(payload, ensure_ascii=False), encoding='utf-8')\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/messages \\\n -H "x-api-key: $AILOOT_API_KEY" -H 'anthropic-version: 2023-06-01' -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\n\nresult = json.loads(Path('result.json').read_text(encoding='utf-8'))\nprint(json.dumps(result, ensure_ascii=False, indent=2))\nPY_RESULT\nimport json, os\nfrom pathlib import Path\n\nsource_file = Path(os.environ['AILOOT_TEXT_FILE'])\nif not source_file.is_file():\n raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')\ntext = source_file.read_text(encoding='utf-8')\nif not text.strip():\n raise SystemExit('Choose a document containing text')\npayload = {'model': 'poolside/laguna-s-2.1', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]}\npayload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\\n\\n' + text\nfrom urllib.request import Request, urlopen\nfrom urllib.error import HTTPError\n\napi_key = os.environ['AILOOT_API_KEY']\nif not api_key.strip():\n raise SystemExit('Set AILOOT_API_KEY before making a request')\nheaders = {'x-api-key': 'PLACEHOLDER_KEY', 'anthropic-version': '2023-06-01', 'Content-Type': 'application/json'}\nheaders['x-api-key'] = api_key\nrequest = Request('https://api.ailoot.ai/v1/messages', data=json.dumps(payload, ensure_ascii=False).encode('utf-8'), headers=headers, method='POST')\ntry:\n with urlopen(request, timeout=120) as response:\n raw = response.read()\n Path('response-headers.txt').write_text(str(response.headers), encoding='utf-8')\nexcept HTTPError as error:\n Path('result.json').write_bytes(error.read())\n Path('response-headers.txt').write_text(str(error.headers), encoding='utf-8')\n raise\nPath('result.json').write_bytes(raw)\nimport json\nfrom pathlib import Path\n\nresult = json.loads(Path('result.json').read_text(encoding='utf-8'))\nprint(json.dumps(result, ensure_ascii=False, indent=2))\nJSON or SSE
Keep every complete returned response, file and content part.
Keep all content blocks, thinking signatures and actual tool-use IDs. Assemble all SSE block deltas; a first text block is not the complete output.
Keep request/response IDs and headers. No generic text job retrieval URL is supplied.
| Field | Type |
|---|---|
Fieldid | Typestring |
Fieldmodel | Typestring |
Fieldcontent[] | Typetext|thinking|tool_use|other content block |
Fieldstop_reason | Typestring|null |
Fieldusage | Typeobject |
| Response | Condition | Next step |
|---|---|---|
Response400 · invalid_request_error | ConditionInvalid required fields, material representation or parameter value | Next stepCorrect the indicated fields before a new creation request. |
Ailoot Ultra poolside/laguna-s-2.1
Use the method paths shown above.
Technical example in English
Propose a measured loop optimization\n\nReview this Python loop: totals = {}\nfor row in rows:\n totals[row["id"]] = sum(x["amount"] for x in rows if x["id"] == row["id"])\n It repeatedly rescans rows. Propose a single-pass replacement preserving per-ID totals. Give a duplicate-ID example and explain how to compare results and timing on the same input without claiming a speed measurement.A single-pass aggregation patch, correctness example and a baseline comparison procedure.Check repeated IDs accumulate all amounts and the proposal contains no fabricated timing improvement.