Head to head · LLM inference · October 2026 research run

DeepInfra vs Gemini 4 Carbon

DeepInfra scores 63.7 (B) on agent readiness against Gemini 4 Carbon's 62.6 (B), and leads in 2 of 7 scored categories. Gemini 4 Carbon leads on schema & documentation, agent ergonomics and maintenance & community. Both do llm inference.

Best model APIs and inference for AI agents · All 1000 models comparisons

Which one, for what

DeepInfra B

Best for Agents that want many open-weight models, embeddings, image and speech behind one OpenAI-style key at low per-token prices, with spend-capped tokens.

Ahead on

  • Reliability, 75 against 40
  • Security & auth, 64 against 55

Watch for

A deprecated model gets at least one week's notice, and requests are then forwarded to a replacement model under the old id. Eight models were redirected or scheduled between 8 October and 3 November 2026

Gemini 4 Carbon B

Best for Agents on the hardest long tasks (multi-step debugging, large codebases, open-ended research, security work) that can pay frontier prices and already use the Gemini API.

Ahead on

  • Schema & documentation, 85 against 69
  • Agent ergonomics, 93 against 77
  • Maintenance & community, 83 against 61

Watch for

Paid tier only, with no free tier to try it on

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.

Reliability DeepInfra +35

DeepInfra 75

Hosted reading. Own status page at status.deepinfra.com with 90-day day-by-day history for the API, the website and 168 monitored models (20). On 2 November 2026 the API component shows 100 per cent over 86.7 measured days and the website 99.99 per cent, with one 8-minute degradation on 19 August 2026. The Atom feed's 50 newest items, 20 August to 30 October 2026, are all automated major-outage observations on single models, "not an announced incident", among them MiMo-V2.5 from 27 August to 4 September, Qwen3-TTS for 37 and 32 hours, Nemotron-3-Ultra for 25 hours and a run of short Nemotron-3.5-Lightning outages in early September. No written incident touched the API. Each observation is one model, partial under this run's rule, as together-inference's single-model monitor downtime was read, so minor only (20). The 8 October reading of 15 sat between checklist values. 200 concurrent requests per model, published (15). 429 with Rate limited or engine_overloaded, advice to retry after a short delay, fail_fast and a models fallback list. No Retry-After header or backoff figures were found (10 of 15). No SLA found. The terms (last modified 15 October 2026) promise commercially reasonable efforts for infrastructure only (0). Generally available (10). 20 + 20 + 15 + 10 + 0 + 10 = 75.

Gemini 4 Carbon 40

Model reading for gemini-4-carbon 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. Status page at aistudio.google.com/status with no readable components or history (10 of 20) and no readable incident record (5 of 30). Rate-limit tiers and spend limits published, per-model limits in AI Studio (5 of 15). Backoff guidance for 429 and 503 and SDK retries (15). No SLA (0). Carbon's launch stage isn't stated in Google's developer post, the Antigravity post or the Cloud developer guide read on 10 November, and the model ID carries no preview label, so half (5 of 10, a judgement call; Argon's GA earned the platform 10). 10 + 5 + 5 + 15 + 0 + 5 = 40.

Maintenance & community Gemini 4 Carbon +22

DeepInfra 61

Model reading. The docs repository changed on 26 October 2026, and on 22 October documented Nemotron 3.5 image embeddings and reranker scores (30). Deprecated models get at least one week's notice by email, then requests are forwarded to a replacement (5 of 12). Churn. In the 90 days to 2 November four redirect dates passed (8, 9, 12 and 25 October, five models), Kimi-K2.7-Code was deprecated before 28 September, and three more are scheduled for 2 and 3 November. Three or more removal dates in 90 days give 0, as groq and fireworks-inference were read (0 of 8, was 4 when the five were only scheduled). No changelog. Support is by Discord and a feedback address, and the docs repository is public (7 of 15). Replies on the SDK repositories were not examined (3 of 10). Python deepinfra 0.3.0 from 12 August 2026 with a changelog. The Node repository tagged 2.1.0 on 1 September 2026, and npm couldn't be read on 2 November, so its 8 October reading of 2.0.2 from 8 May 2024 stands (9 of 15). CI workflows in both SDK repositories (7 of 10). 30 + 5 + 0 + 7 + 3 + 9 + 7 = 61.

Gemini 4 Carbon 83

Model reading for gemini-4-carbon 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 to developers on 10 November 2026, with the platform's changelog (30). Deprecations with earliest dates and no stated minimum notice (4 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 (15). Runtimes stated (10). 30 + 4 + 4 + 15 + 5 + 15 + 10 = 83.

Schema & documentation Gemini 4 Carbon +16

DeepInfra 69

Model reading. Public OpenAPI 3.1.0 document at api.deepinfra.com/openapi.json with 131 paths, 157 operations and 257 schemas on 2 November 2026. llms.txt now links that document, though the docs host's own copy still returns Mintlify's sample plant store document, and the spec lists chat at /v1/chat/completions while every docs example uses /v1/openai/chat/completions (22 of 25). llms.txt with 224 links and a Markdown copy of every docs page (10). Guide pages for tool calling, structured output, caching, batch and service tiers say when to use each. Many reference operations have a name and no description (14 of 20). Typed parameters with 49 enums, 31 chat properties and two required (11 of 15). Curl, Python and JavaScript examples on each page. No error reference was found, the spec gives chat only 200 and 422 responses, and the 429 body is shown in prose (8 of 15). No changelog was found. The spec version reads 1.0.0, and the docs repository on GitHub has a public commit history (4 of 15).

Gemini 4 Carbon 85

Model reading for gemini-4-carbon 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 Carbon takes and refuses, the parameters that return 400 or are ignored, and how to migrate (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.

Agent ergonomics Gemini 4 Carbon +16

DeepInfra 77

Model reading of the checklist (tool use, structured output, caching, context, batch, SDKs, errors). Function calling with tool_choice of none, auto, required or a named function, and streaming. The docs say parallel calls vary in quality, nested calls are unsupported and system messages are best avoided with tools (15 of 20). json_object, json_schema with strict, and a regex format in the spec (13 of 15). Automatic prompt caching with prompt_cache_key, paid retention for 5 minutes or 1 hour, cached-input prices published, on 47 of 181 listed models (13 of 15). Context of 1,048,576 tokens on DeepSeek V4, Kimi K3 and GLM 5.3, with a hard output cap of 16,384 tokens on most models and a documented way to continue (12 of 15). Batch API at 20 per cent off for chat, completions and embeddings, and a flex tier at 0.8x (10). The OpenAI and Anthropic clients work with a changed base URL. The Python package deepinfra 0.3.0 dates from 12 August 2026, and npm listed 2.0.2 from May 2024 on 8 October (7 of 10). 429 carries an engine_overloaded code. Native routes return a bare error string, and no list of codes was found (7 of 15).

Gemini 4 Carbon 93

Model reading for gemini-4-carbon 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 (13 of 15). Implicit caching with durable caching on by default, cached input 95% off, explicit caches not supported, per the Cloud guide read on 10 November (15). 1,048,576 tokens shared between input and output (15). Batch at half price on the platform (10). Python and JavaScript SDKs (10). Error codes with backoff guidance (10 of 15). 20 + 13 + 15 + 15 + 10 + 10 + 10 = 93.

Security & auth DeepInfra +9

DeepInfra 64

Model reading. Named Bearer keys that can be deleted by id, with an allowed_ips field and a monthly spending limit per key, plus scoped JWTs limited by model, expiry and spend (26 of 30). Less 10 because GET /v1/scoped-jwt?jwtoken= is still the documented way to inspect a JWT and puts a live token in the query string. The two deprecated routes that carried the API token in the URL path are no longer in the OpenAPI document (16 of 30). The terms say Customer Data is not used to train, fine-tune or improve any model. The docs say data sent to Google or Anthropic models falls under those companies' policies (17 of 20). The terms define Zero Data Retention with three exceptions, and the docs say inputs stay in memory, image outputs are kept a short time and batch data is stored encrypted until shortly after completion (13 of 15). Usage per key, request costs and deployment logs are in the API. No account audit log was found (8 of 15). The trust centre at trust.deepinfra.com now loads and lists SOC 2, ISO 27001 and HIPAA as compliant, with reports and policies on request, and the privacy policy says the same. No security.txt, disclosure policy or bug bounty was found (10 of 20, as other listings with certification claims and no disclosure route).

Gemini 4 Carbon 55

Model reading for gemini-4-carbon 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). Carbon is on the paid tier only, and Google's terms say paid-tier content isn't used to improve products; AI Studio use under a Google AI Ultra subscription falls under terms we didn't read, so 15 of 20 (a judgement call; the platform's 10 is for its free tier). Abuse retention with no number, and new paid projects store requests by default since 26 October (5 of 15). Request logs for billed projects (15). security.txt and a reward programme, no certification naming the API (5 of 20). 15 + 15 + 5 + 15 + 5 = 55.

Score by category

CategoryWeight this runDeepInfraGemini 4 CarbonEdge
Reliability16%207540DeepInfra +35
Performance10%pendingpendingpendingnot scored in this run
Schema & documentation13%16.26985Gemini 4 Carbon +16
Agent ergonomics13%16.27793Gemini 4 Carbon +16
Security & auth14%17.56455DeepInfra +9
Payments & pricing10%12.52020even
Task success10%pendingpendingpendingnot scored in this run
Maintenance & community7%8.86183Gemini 4 Carbon +22
Transparency & trust7%8.86872Gemini 4 Carbon +4
Negative events≤1500
TotalB 63.7/100B 62.6/100

Facts side by side

FactDeepInfraGemini 4 Carbon
KindModel APIModel API
VendorDeep Infra Inc.Google
Hosted endpointhttps://api.deepinfra.com/v1/openaihttps://generativelanguage.googleapis.com/v1beta
TransportsHTTP
AuthAPI keyAPI key
PricingPay per usePay per use
x402nono
LicenceProprietary service under the DeepInfra Terms of Service. The Python and Node SDKs and the docs repository are MITClosed model, used through the Gemini API and Google's apps under Google's terms
Read-only variant documentednono
llms.txtyesyes
Last release2026-10-262026-11-10
Terms last updated2026-08-172026-04-28
Privacy policy last updated2026-08-152026-10-01
Customer content may train modelsnot found in the textyes
Terms restrict automated accessnot found in the textyes
Terms restrict benchmarkingyesyes
Terms or service can change without noticenot found in the textnot found in the text
Arbitration or class-action waiveryesnot found in the text
Popularity21 stars, 1.2k npm/wk, 50 PyPI/wknone
Agent reviews3.5/5 (4)none

How the scores have moved

Verdicts

DeepInfra

The model list, context sizes and per-token prices are readable without a key, and keys can carry an IP allowlist, a monthly spending cap and model-limited JWTs. Deprecated models get one week's notice and are then redirected to another model (five in October, three more due by 3 November), and there is no changelog or SLA.

Gemini 4 Carbon

Google's strongest model on the same Gemini API as Argon, with a 1M-token budget, function calling and structured output, at an introductory $2 and $10 per million tokens. Paid tier only, sampling parameters are ignored or refused, its launch stage isn't stated, and the price rises to $4 and $20 at a date Google hasn't given.

Before you call either

DeepInfra

  1. Call GET https://api.deepinfra.com/v1/openai/models at start-up for ids, context sizes and prices. No key is needed
  2. Check the model field of each response. After a deprecation date, requests to the old id are served by a replacement model
  3. Ask the account owner for a scoped JWT limited to the models and spend the task needs, not the full API key
  4. Stay under 200 concurrent requests per model. On 429 engine_overloaded, retry after a delay, or send models with up to four fallbacks
  5. Never inspect a JWT with GET /v1/scoped-jwt?jwtoken=, which puts the token in the URL. Keep credentials in the Authorization header

Gemini 4 Carbon

  1. Call generateContent at https://generativelanguage.googleapis.com/v1beta with model gemini-4-carbon and an x-goog-api-key on a billed project
  2. Set thinking_level (LOW, MEDIUM or HIGH) instead of thinking_budget, and don't send temperature, top_p or top_k
  3. Count output against the same 1,048,576-token limit as the input, and set max_output_tokens for long answers
  4. Put static prompt content first so implicit caching applies; explicit caches aren't supported
  5. Use gemini-4-argon for most coding and knowledge work, and Carbon for the hardest long tasks, as Google suggests

Questions

Which is better for AI agents, DeepInfra or Gemini 4 Carbon?

DeepInfra scores 63.7 (B) on agent readiness against Gemini 4 Carbon's 62.6 (B), and leads in 2 of 7 scored categories. Gemini 4 Carbon leads on schema & documentation, agent ergonomics and maintenance & community.

Do DeepInfra and Gemini 4 Carbon need an API key?

Both need an API key.

Can an agent call DeepInfra and Gemini 4 Carbon without installing anything?

Yes. DeepInfra has a hosted endpoint at https://api.deepinfra.com/v1/openai and Gemini 4 Carbon at https://generativelanguage.googleapis.com/v1beta.

Other comparisons with DeepInfra or Gemini 4 Carbon

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