# Gemini 4 Hydrogen vs Meta Model API

> Gemini 4 Hydrogen scores 64.9 (B) to Meta Model's 53.6 (D) for llm inference. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-meta-model-api
- Markdown: https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-meta-model-api.md (~7,200 tokens)
- Site index for agents: https://www.anchorterminal.com/llms.txt (the key docs and every listing in one file, at most 16 MB, about 4 million tokens: https://www.anchorterminal.com/llms-full.txt)
- API: https://www.anchorterminal.com/api/v1/index.json
- MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key)
- DNS: dig +short TXT <slug>.grade.anchorterminal.com (the grade as a DNS TXT record, for a sandbox with no HTTP where names still resolve; also top., best.<category>. and <a>.vs.<b>. under the zone, and the same under grade.letme.dev)
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)
- Updated: 2026-11-13

Gemini 4 Hydrogen scores 64.9 (B) on agent readiness against Meta Model API's 53.6 (D), and leads in 6 of 7 scored categories. Meta Model API leads on security & auth. Both do llm inference.

- Gemini 4 Hydrogen: grade B, 64.9/100, rank #546 of 1524. Markdown https://www.anchorterminal.com/tools/gemini-4-hydrogen.md · JSON https://www.anchorterminal.com/api/v1/tools/gemini-4-hydrogen.json
- Meta Model API: grade D, 53.6/100, rank #1017 of 1524. Markdown https://www.anchorterminal.com/tools/meta-model-api.md · JSON https://www.anchorterminal.com/api/v1/tools/meta-model-api.json
- Best model APIs and inference for AI agents: https://www.anchorterminal.com/best/inference/index.md
- All 1044 models comparisons: https://www.anchorterminal.com/compare/inference/index.md

## Which one, for what

### Gemini 4 Hydrogen (B)

Good for: Agents doing everyday coding, tool calls and document work at volume on the Gemini API, including ones that need to start on a free key, or as the executor beside Carbon or Argon.

Ahead on:
- Reliability, 45 against 40
- Schema & documentation, 85 against 60
- Agent ergonomics, 90 against 75
- Payments & pricing, 40 against 30
- Maintenance & community, 85 against 46
- Transparency & trust, 72 against 53

Watch for: temperature, top_p and top_k are ignored, and thinking_budget, MINIMAL, penalties and extra candidates return a 400

### Meta Model API (D)

Good 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:
- Security & auth, 64 against 50

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


## Why the edge

### Maintenance & community: Gemini 4 Hydrogen +39

- Gemini 4 Hydrogen (85): Model reading for gemini-4-hydrogen on the Gemini API. Platform facts are the Gemini API listing's (gemini-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. Released on 12 November 2026, recorded in Google Cloud's release notes; the Gemini API changelog, last updated 30 October, has no entry for it yet (30). Google Cloud states a minimum of 45 days' notice before Hydrogen is retired, short and stated for Cloud only; the Gemini API's deprecations give earliest dates with no stated minimum (6 of 12). Shutdowns in the last 90 days kept to their table (4 of 8). Dated changelog (15). SDK replies not sampled (5 of 10). Current SDKs; the developer guide names the minimum google-genai, @google/genai, Go and Java versions (15). Runtimes stated (10). 30 + 6 + 4 + 15 + 5 + 15 + 10 = 85.
- 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.

### Schema & documentation: Gemini 4 Hydrogen +25

- Gemini 4 Hydrogen (85): Model reading for gemini-4-hydrogen on the Gemini API. Platform facts are the Gemini API listing's (gemini-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. Discovery document for GenerateContent (20 of 25). llms.txt with Markdown twins (10). The Cloud developer guide says what Hydrogen takes, every parameter that's ignored or returns 400, the thought-signature rules and how to migrate from Gemini 3.8 Flash, though it doesn't document forced calls (15 of 20). Structured output to a JSON Schema with some keywords not enforced (10 of 15). Examples and an errors page (15). Dated changelog and model versions (15). 20 + 10 + 15 + 10 + 15 + 15 = 85.
- 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.

### Transparency & trust: Gemini 4 Hydrogen +19

- Gemini 4 Hydrogen (72): Model reading for gemini-4-hydrogen on the Gemini API. Platform facts are the Gemini API listing's (gemini-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. Closed service with clear terms, SDKs Apache-2.0 (15). Free-tier content is used to improve products, paid-tier content isn't, abuse retention has no number, and request storage is on by default for new paid projects; the statements agree with each other (20 of 30). Deprecations table, and a stated 45-day minimum on Cloud (15 of 20). Data may be cached anywhere Google has facilities; no subprocessor list checked (5 of 20). A DeepMind model card gives a March 2026 knowledge cutoff, automated safety results and Frontier Safety Framework outcomes, with the full safety report marked coming soon. Every benchmark figure is Google's own. 15 + 20 + 15 + 5 = 55.
- 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.

### Agent ergonomics: Gemini 4 Hydrogen +15

- Gemini 4 Hydrogen (90): Model reading for gemini-4-hydrogen on the Gemini API. Platform facts are the Gemini API listing's (gemini-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. Function calling, with forced calls by FunctionCallingConfig on the API (20). Structured output to a response_schema, listed as supported on the Cloud model page (13 of 15). Implicit caching on prefixes of 4,096 tokens or more with durable caching on by default and explicit caches not supported, per the Cloud model page; the announcement names no cached-input price (12 of 15). 1,048,576 tokens shared between input and output, with background long decode up to 1M (15). Batch at half price on the platform (10). Python and JavaScript SDKs, and an OpenAI-compatible Chat Completions surface (10). Error codes with backoff guidance (10 of 15). 20 + 13 + 12 + 15 + 10 + 10 + 10 = 90.
- 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.

### Security & auth: Meta Model API +14

- Gemini 4 Hydrogen (50): Model reading for gemini-4-hydrogen on the Gemini API. Platform facts are the Gemini API listing's (gemini-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. Keys bound to a service account and restricted to the Gemini API, with no scopes and a token accepted in the URL (15 of 30, the platform's deduction). Hydrogen is on the free tier, where Google's terms say content is used to improve products and may be read by humans; paid-tier content isn't (10 of 20, the platform's reading; Carbon, paid tier only, got 15). Abuse retention has no number and new paid projects store requests by default since 26 October; Google's prompt-injection figure (2.3 times more resilient on Gray Swan's benchmark) is its own, and the model card says jailbreak resistance is still being improved (5 of 15). Request logs for billed projects (15). security.txt and a reward programme, no certification naming the API (5 of 20). 15 + 10 + 5 + 15 + 5 = 50.
- 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.

### Payments & pricing: Gemini 4 Hydrogen +10

- Gemini 4 Hydrogen (40): Model reading for gemini-4-hydrogen on the Gemini API. Platform facts are the Gemini API listing's (gemini-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. No machine payment protocol (0). Prices published in Google's announcement without a login, $1.50 input and $7.50 output per million tokens, flat across the window; the Gemini API pricing page, now at aistudio.google.com, is still to be read for Hydrogen's row (20). A free tier with no card: Google's announcement puts Hydrogen on all tiers of the Gemini API, and the free tier needs no billing account per the gemini-api listing; its limits for Hydrogen are on that pricing page (20). A Google account and AI Studio for a key (0). 0 + 20 + 20 + 0 = 40.
- Meta Model API (30): No machine payment protocol (0). Per-token prices for Muse Wonder and Muse Spark, and per-image, per-hour and per-frame prices for the other models, published without a login (20). No free API allowance found. Billing needs a card, and Playground requests count towards usage (0). Browser sign-up with email, phone, Facebook, Google or GitHub (0). 0 + 20 + 0 + 0 = 20. Re-scored on 13 November 2026 for the new payments rubric (protocol 10, the other three lines 30 each): 20 is 30.

## Score by category

| Category | Weight | Gemini 4 Hydrogen | Meta Model API | Edge |
| --- | --- | --- | --- | --- |
| Reliability | 16% (20 this run) | 45 | 40 | Gemini 4 Hydrogen +5 |
| Performance | 10%, pending | pending | pending | not scored in this run |
| Schema & documentation | 13% (16.2 this run) | 85 | 60 | Gemini 4 Hydrogen +25 |
| Agent ergonomics | 13% (16.2 this run) | 90 | 75 | Gemini 4 Hydrogen +15 |
| Security & auth | 14% (17.5 this run) | 50 | 64 | Meta Model API +14 |
| Payments & pricing | 10% (12.5 this run) | 40 | 30 | Gemini 4 Hydrogen +10 |
| Task success | 10%, pending | pending | pending | not scored in this run |
| Maintenance & community | 7% (8.8 this run) | 85 | 46 | Gemini 4 Hydrogen +39 |
| Transparency & trust | 7% (8.8 this run) | 72 | 53 | Gemini 4 Hydrogen +19 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **64.9 · B** | **53.6 · D** | |

## Facts side by side

| Fact | Gemini 4 Hydrogen | Meta Model API |
| --- | --- | --- |
| Kind | Model API | Model API |
| Vendor | Google | Meta |
| Hosted endpoint | `https://generativelanguage.googleapis.com/v1beta` | `https://api.meta.ai/v1` |
| Transports |  | HTTP |
| Auth | API key | API key |
| Pricing | Pay per use | Pay per use |
| x402 | no | no |
| Licence | Closed model, used through the Gemini API and Google's apps under Google's terms | Closed 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 documented | no | no |
| llms.txt | yes | yes |
| Last release | 2026-11-12 | 2026-10-12 |
| Terms last updated | 2026-04-28 | 2026-10-02 |
| Privacy policy last updated | 2026-10-01 | couldn't be read |
| Customer content may train models | yes | yes |
| Terms restrict automated access | yes | not found in the text |
| Terms restrict benchmarking | yes | yes |
| Terms or service can change without notice | not found in the text | not found in the text |
| Arbitration or class-action waiver | not found in the text | yes |

## How the scores have moved

### Gemini 4 Hydrogen

No score change since it was listed.

### Meta Model API

- +1.3 on 13 November 2026: checklist
- -0.2 on 30 October 2026: checklist
- +0.3 on 27 October 2026: checklist

## Verdicts

**Gemini 4 Hydrogen.** Google's fast Gemini 4 model, on every tier of the Gemini API including the free one, at $1.50 and $7.50 per million tokens with a 1M-token budget, four thinking levels, function calling and structured output. Sampling parameters are ignored, explicit caching isn't supported, it's a short-term release with 45 days' notice on Cloud, and the benchmarks are Google's.

**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

### Gemini 4 Hydrogen

1. Call generateContent at https://generativelanguage.googleapis.com/v1beta with model gemini-4-hydrogen and an x-goog-api-key
2. Set thinking_level instead of thinking_budget; the default is HIGH, so set MEDIUM to keep 3.8 Flash's latency
3. Don't send temperature, top_p, top_k, penalties or candidate_count above 1
4. Pass every thought_signature back in its original part, on the first parallel functionCall only, or the call returns a 400
5. Put static content of 4,096 tokens or more first so implicit caching applies, and keep confidential prompts off the free tier

### 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, Gemini 4 Hydrogen or Meta Model API?

Gemini 4 Hydrogen scores 64.9 (B) on agent readiness against Meta Model API's 53.6 (D), and leads in 6 of 7 scored categories. Meta Model API leads on security & auth.

### Do Gemini 4 Hydrogen and Meta Model API need an API key?

Both need an API key.

### Can an agent call Gemini 4 Hydrogen and Meta Model API without installing anything?

Yes. Gemini 4 Hydrogen has a hosted endpoint at https://generativelanguage.googleapis.com/v1beta and Meta Model API at https://api.meta.ai/v1.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-meta-model-api.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-meta-model-api.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "gemini-4-hydrogen", "b": "meta-model-api"}`. From a terminal: `anchor compare gemini-4-hydrogen meta-model-api`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/gemini-4-hydrogen.json and https://www.anchorterminal.com/api/v1/tools/meta-model-api.json

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- [Meta Model API vs Nemotron 3.5 Super VL](https://www.anchorterminal.com/compare/meta-model-api-vs-nemotron-3-5-super-vl.md)
- [Meta Model API vs Novita AI](https://www.anchorterminal.com/compare/meta-model-api-vs-novita-ai.md)
- [Meta Model API vs OpenAI API](https://www.anchorterminal.com/compare/meta-model-api-vs-openai-api.md)
- [Meta Model API vs OpenRouter](https://www.anchorterminal.com/compare/meta-model-api-vs-openrouter.md)
- [Meta Model API vs Prism Inference](https://www.anchorterminal.com/compare/meta-model-api-vs-prism-inference.md)
- [Meta Model API vs Router.com (Ramp Router)](https://www.anchorterminal.com/compare/meta-model-api-vs-ramp-router.md)
- [Meta Model API vs Beam 1.0](https://www.anchorterminal.com/compare/meta-model-api-vs-reflection-beam.md)
- [Meta Model API vs RouterPlus](https://www.anchorterminal.com/compare/meta-model-api-vs-routerplus.md)
- [Meta Model API vs SambaCloud](https://www.anchorterminal.com/compare/meta-model-api-vs-sambanova.md)
- [Meta Model API vs SiliconFlow](https://www.anchorterminal.com/compare/meta-model-api-vs-siliconflow.md)
- [Meta Model API vs Simplismart](https://www.anchorterminal.com/compare/meta-model-api-vs-simplismart.md)
- [Meta Model API vs StepFun API](https://www.anchorterminal.com/compare/meta-model-api-vs-stepfun-api.md)
- [Meta Model API vs SWE-2 Flash](https://www.anchorterminal.com/compare/meta-model-api-vs-swe-2-flash.md)
- [Meta Model API vs Tencent Cloud TokenHub](https://www.anchorterminal.com/compare/meta-model-api-vs-tencent-tokenhub.md)
- [Meta Model API vs Together AI Inference](https://www.anchorterminal.com/compare/meta-model-api-vs-together-inference.md)
- [Meta Model API vs Vercel AI Gateway](https://www.anchorterminal.com/compare/meta-model-api-vs-vercel-ai-gateway.md)
- [Meta Model API vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/meta-model-api-vs-vertex-ai.md)
- [Meta Model API vs xAI API (Grok)](https://www.anchorterminal.com/compare/meta-model-api-vs-xai-api.md)

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