Head to head · LLM inference · October 2026 research run

Inkling 2 Small vs Meta Model API

Meta Model API and Inkling 2 Small score within a point of each other on agent readiness, 52.3 (D) and 52.1 (D). Inkling 2 Small leads on schema & documentation, maintenance & community and transparency & trust. Both do llm inference.

Best model APIs and inference for AI agents · All 596 models comparisons

Which one, for what

Inkling 2 Small D

Best for Developers who want a capable multimodal open-family model through an OpenAI-compatible API today, or to fine-tune it on Tinker, and who can live with a beta surface until the weights are published.

Ahead on

  • Schema & documentation, 70 against 60
  • Maintenance & community, 75 against 46
  • Transparency & trust, 64 against 53

Watch for

Serverless inference and the OpenAI-compatible API are both labelled beta, with a waitlist for higher throughput

Meta Model API D

Best for Long-running agent and coding work over text, images, video, audio and PDFs with a 1M-token context, where training must stay off by default.

Ahead on

  • Reliability, 40 against 30
  • Agent ergonomics, 75 against 62
  • Security & auth, 64 against 57

Watch for

Muse Wonder is labelled a preview, and no deprecation policy or notice period was found

Why the edge

Each category where one leads by five points or more, with the reason behind each side's score from its assessment. The numbers are in the table below; this is what they stand for.

Maintenance & community Inkling 2 Small +29

Inkling 2 Small 75

Model reading. Released 5 November 2026 (30). Churn and notice in place of release counts: Inkling, Inkling-Small (30 July) and Inkling 2 Small in about four months, and a deprecations page with dated retirements and a promise to 'aim to give advance notice' by email, with no stated period, per the tinker listing (10 of 20). A public changelog and a support address and Discord, per the tinker listing (10 of 15). Current official SDKs, Tinker's Python SDK and the OpenAI-compatible endpoint (15). SDK and cookbook CI, per the tinker listing (10). 30 + 10 + 10 + 15 + 10 = 75.

Meta Model API 46

Model reading. Muse Wonder launched on 12 October 2026 (30). No deprecation policy or notice period found. All three Muse Spark versions are still served, so no retirement has tested one (0 of 12). Dated launches on the API since 6 August that we read, Muse Image on 25 August, Muse Voice Transcribe on 3 September and Muse Wonder on 12 October (8 of 8). Muse Code leaving beta on 31 August is a separate product and isn't counted. No Model API changelog. A help centre with support tickets, answered 'typically within two business days' by Meta's own account, which we didn't test (8 of 15). No SDK of Meta's own for the API (0 of 15, 0 of 10). 30 + 0 + 8 + 8 + 0 = 46.

Agent ergonomics Meta Model API +13

Inkling 2 Small 62

Model reading (tool use, structured output, caching, context, batch, SDKs). A 256K served context, max_tokens and a priced cache at $0.03 per million; tool use isn't documented for this model (15 of 25). Output-size controls through max_tokens, reasoning_effort and separate_reasoning (15 of 20). One documented error, the 400 for unsupported reasoning_effort (5 of 20). Stable request IDs make SDK sampling retries safe, per the tinker listing (15 of 20). OpenAI's SDKs work against the endpoint, with Tinker's Python SDK besides, and reasoning defaults to 0.9 (12 of 15). 15 + 15 + 5 + 15 + 12 = 62.

Meta Model API 75

Model reading of the checklist (tool use, structured output, caching, context, batch, SDKs, errors). Tool calls on all three endpoints, parallel calls, tool search with deferred loading, built-in web search and a computer-use tool, but tool_choice accepts only auto, so a call can't be forced or forbidden (15 of 20). Output constrained to the JSON Schema on every surface (15). Automatic prompt caching with no setup, and cached input at 5 per cent of the input price on Muse Wonder (15). 1,048,576-token context on Muse Wonder and every Muse Spark version, with no long-context premium (15). No batch endpoint or discount found. Background responses run asynchronously at the normal price (0). No SDK of Meta's own. The docs use the OpenAI and Anthropic SDKs against compatible endpoints, the same reading as StepFun (0). OpenAI-style error envelope with type and code, per-status fixes and named retryable codes (15). 15 + 15 + 15 + 15 + 0 + 0 + 15 = 75.

Transparency & trust Inkling 2 Small +11

Inkling 2 Small 64

Editorial half. Closed for now under the Tinker Terms of Use and the Model Acceptable Use Policy, per the model card, with Apache 2.0 weights promised the week after launch (15). Terms commit not to use Customer Content for training, keep it only as needed, with a DPA and deletion on request; the privacy notice wasn't re-read, per the tinker listing (15). Dated deprecation notices with no stated period (15). The DPA annex names Google Cloud, Cloudflare, WorkOS and OpenAI with service and location (15). 15 + 15 + 15 + 15 = 60.

Meta Model API 53

Closed service with clear terms, under California law with AAA arbitration for Meta Platforms, Inc. and Irish law and courts for Meta Platforms Ireland Limited, with liability capped at the greater of 12 months of fees or $1,000. Muse Glimmer's open weights don't change the hosted service (15). The terms, the models page, the pricing page and the zero-retention help page agree that Standard content isn't used for training and Contributor content may be, but retention periods are not stated outside zero retention, and the privacy policy is Meta's general one (18 of 30). No deprecation policy or dated notices found, and the terms let Meta remove functionality without notice (0). A subprocessor list is linked from the terms but wasn't read, and no data locations were found (8 of 20). 15 + 18 + 0 + 8 = 41.

Reliability Meta Model API +10

Inkling 2 Small 30

Model reading, for the surface it's called on, Tinker's serverless inference and OpenAI-compatible API. No status page found, per the tinker listing's checks of 1 October and 4 November (0), so no readable incident history (5). The only published limit is the in-flight sample cap of 2,000, per the tinker listing's 1 October check; the OpenAI-compatible page gives no numbers and says higher or more stable throughput needs the waitlist (10). The SDK retries sampling with stable request IDs, per the tinker listing's reading of the SDK repository; the OpenAI-compatible page has no retry guidance (15). No SLA found (0). Serverless inference is 'currently in beta' on the models and pricing page and the OpenAI-compatible API is labelled Beta, read 6 November (0). 0 + 5 + 10 + 15 + 0 + 0 = 30.

Meta Model API 40

No status page with readable component history. The error guide's dev.meta.ai/status link now opens a Model API Status page, which showed "Status information is unavailable" to our reader on 4 November, its content drawn by script that we don't request, and the documented GET /v1/status reports only the current state (0). No readable incident history (5). Rate limits published per team and per model, 3,000 RPM and 4,000,000 TPM for muse-wonder-1.0, 150 RPM and 3,000,000 TPM for the contributor variants, 600 background submissions a minute (15). 429 and 503 carry Retry-After (60 seconds for service_overloaded and backend_unavailable), with backoff-with-jitter guidance, rate-limit headers on every token response and a documented mid-stream server_shutting_down event to retry, a generation call read as stateless as gemini-api's (15). No SLA found, and the terms let Meta throttle or suspend access without notice (0). Muse Wonder is labelled a preview. Muse Spark 1.3 carries no preview label, but the API opened on 8 July 2026 as a public preview and we found no general-availability notice, so half (5 of 10, the 12 October partial, kept). 0 + 5 + 15 + 15 + 0 + 5 = 40.

Schema & documentation Inkling 2 Small +10

Inkling 2 Small 70

Model reading. No OpenAPI file of its own; the OpenAI-compatible chat and completions endpoints and the typed Python SDK are the contract, as the tinker listing reads it (15). llms.txt on the docs host, per the tinker listing's 1 October check (10). The model card states intended use (agentic and tool-use systems, coding assistants, chatbots, RAG), inputs, outputs and limitations, and the launch post its benchmarks (16 of 20). Model IDs, context lengths and the reasoning_effort values (none to xhigh, or 0.0 to 0.99) are documented; the tool-call format and structured output aren't, for this model (8 of 15). A Python example and an HTTP 400 for models without reasoning_effort, with no error page (6 of 15). A dated Tinker changelog and a model deprecations page, per the tinker listing (15). 15 + 10 + 16 + 8 + 6 + 15 = 70.

Meta Model API 60

No OpenAPI or similar machine-readable contract found. The reference is Markdown pages with schema sections, and /openapi.json paths on dev.meta.ai return 404 (0). llms.txt at dev.meta.ai/llms.txt, and every docs page has a Markdown copy (10). The models page says when to choose Muse Wonder and when Muse Spark, and the tool-calling and structured-output guides state their limits, such as tool_choice only accepting auto and which schema keywords strict allows (16 of 20). JSON Schema tools and output, reasoning_effort an enum from minimal to max, a strict subset that returns 400 when violated, though tool_choice has one value (12 of 15). Curl, Python and TypeScript examples on each page and an error guide covering 400 to 504 with the error envelope, codes and a fix for each (15). Versioned model IDs (muse-spark-1.1 to 1.3, muse-wonder-1.0) and dated launch posts on the developer blog, but no API changelog (7 of 15). 0 + 10 + 16 + 12 + 15 + 7 = 60.

Security & auth Meta Model API +7

Inkling 2 Small 57

Platform readings are the tinker listing's. Revocable API keys from the console, with no per-key scopes found (20). Org, team and project permissions with no read-only role (5). The model returns its own completions, no third-party content (10). get_audit_log() for admins (15). security.txt with a reporting address and no stated acknowledgement time (7). For the model reading, Tinker's terms commit not to use Customer Content for training, recorded under transparency. 20 + 5 + 10 + 15 + 7 = 57.

Meta Model API 64

Model reading. Bearer keys per team, named, shown once and revoked by deletion, with rotation advice. No scopes, read-only keys or per-key limits found (20 of 30). The terms say Meta will not use Content submitted to the Standard Services to train Meta Models, and training applies only to the -contributor model IDs a caller picks. Content may still be used to develop and improve safety and security systems (17 of 20). Retention is 'as needed' for the service, the law, flagged violations and safety review, with no period stated. Zero data retention for qualified accounts through sales, with flagged content kept up to 2 years (10 of 15). The usage dashboard shows tokens and requests per day by model, with no per-key or per-call log found (5 of 15). No security.txt on dev.meta.ai. Meta's bug bounty at bugbounty.meta.com is named in the Muse Code SDK's security policy, and a security commitments page describes TLS 1.2 or later, PCI-DSS card handling and an ISMS, with no named certification for the API (12 of 20). 20 + 17 + 10 + 5 + 12 = 64.

Score by category

CategoryWeight this runInkling 2 SmallMeta Model APIEdge
Reliability16%203040Meta Model API +10
Performance10%pendingpendingpendingnot scored in this run
Schema & documentation13%16.27060Inkling 2 Small +10
Agent ergonomics13%16.26275Meta Model API +13
Security & auth14%17.55764Meta Model API +7
Payments & pricing10%12.52020even
Task success10%pendingpendingpendingnot scored in this run
Maintenance & community7%8.87546Inkling 2 Small +29
Transparency & trust7%8.86453Inkling 2 Small +11
Negative events≤1500
TotalD 52.1/100D 52.3/100

Facts side by side

FactInkling 2 SmallMeta Model API
KindModel APIModel API
VendorThinking Machines LabMeta
Hosted endpointhttps://tinker.thinkingmachines.dev/services/tinker-prod/oai/api/v1https://api.meta.ai/v1
TransportsHTTP
AuthAPI keyAPI key
PricingPay per usePay per use
x402nono
LicenceProprietary for now, used through Tinker under the Tinker Terms of Use and Thinking Machines' Model Acceptable Use Policy, per the model card. Thinking Machines says it will publish the weights under Apache 2.0 the week after launch, once outside safety testing is doneClosed service under the Meta Model API Terms of Service (last updated 9 October 2026), with Meta Platforms, Inc., or Meta Platforms Ireland Limited in the European Region, as the contracting party.
Read-only variant documentednono
llms.txtyesyes
Last release2026-11-052026-10-12
Terms last updatednot read yet2026-10-02
Privacy policy last updatednot read yetcouldn't be read
Customer content may train modelsnot read yetyes
Terms restrict automated accessnot read yetnot found in the text
Terms restrict benchmarkingnot read yetyes
Terms or service can change without noticenot read yetnot found in the text
Arbitration or class-action waivernot read yetyes

How the scores have moved

Verdicts

Inkling 2 Small

A new multimodal mixture-of-experts model from Thinking Machines, callable today through Tinker's OpenAI-compatible endpoint at $0.30 and $1.00 per million tokens, with strong vendor-reported coding and agent scores. Tinker's serverless inference is beta, with no status page or SLA, and the weights aren't public yet.

Meta Model API

Meta Model API serves Muse Wonder, in preview since 12 October 2026, and Muse Spark with 1M-token context, constrained JSON Schema output, automatic prompt caching and per-token prices published without a login. Standard-mode content is not used for training. There's no free allowance, status history, OpenAPI file, deprecation policy or way to force a tool call.

Before you call either

Inkling 2 Small

  1. Call https://tinker.thinkingmachines.dev/services/tinker-prod/oai/api/v1 with an OpenAI client and TINKER_API_KEY, using /chat/completions
  2. The serverless model ID is thinkingmachines/Inkling-2-Small:peft:262144:sampling-nvfp4, per Tinker's pricing page. The OpenAI page's examples use your own checkpoint paths, so confirm it before relying on it
  3. Pass reasoning_effort as none to xhigh or 0.0 to 0.99 (default 0.9); reasoning arrives in reasoning_content unless separate_reasoning is false
  4. Keep images between 40 and 4096 px a side, per the model card
  5. Validate JSON yourself, since structured output and the tool-call format aren't documented
  6. Plan for a move to open weights. When they publish, the open-weights listing will carry sizes and runtimes

Meta Model API

  1. Use muse-wonder-1.0 for long multi-step work and muse-spark-1.3 for cheaper, high-volume calls. Both take the same requests
  2. Keep personal or confidential data away from -contributor model IDs. Meta may train on that content
  3. Honour Retry-After on 429 and 503, which is 60 seconds for service_overloaded, and check GET https://api.meta.ai/v1/status, which needs no key
  4. Pass the key to the OpenAI SDK explicitly or set OPENAI_API_KEY. The SDK doesn't read MODEL_API_KEY
  5. Leave tool_choice at auto. The docs say none and required are not supported

Questions

Which is better for AI agents, Inkling 2 Small or Meta Model API?

Meta Model API and Inkling 2 Small score within a point of each other on agent readiness, 52.3 (D) and 52.1 (D). Inkling 2 Small leads on schema & documentation, maintenance & community and transparency & trust.

Do Inkling 2 Small and Meta Model API need an API key?

Both need an API key.

Can an agent call Inkling 2 Small and Meta Model API without installing anything?

Yes. Inkling 2 Small has a hosted endpoint at https://tinker.thinkingmachines.dev/services/tinker-prod/oai/api/v1 and Meta Model API at https://api.meta.ai/v1.

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