Head to head · LLM inference · October 2026 research run
Gemini 4 Carbon vs Nemotron 3.5 Super VL
Gemini 4 Carbon scores 62.6 (B) on agent readiness against Nemotron 3.5 Super VL's 59.3 (C), and leads in 5 of 7 scored categories. Nemotron 3.5 Super VL leads on reliability and payments & pricing. Both do llm inference.
Best model APIs and inference for AI agents · All 1000 models comparisons
Which one, for what
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 74
- Agent ergonomics, 93 against 65
- Maintenance & community, 83 against 71
- Transparency & trust, 72 against 58
Watch for
Paid tier only, with no free tier to try it on
Best for Agents that read documents, charts, screenshots and short videos and need tool calls in one open model, prototyped free on NVIDIA's endpoint and moved to a partner host or own GPUs for production.
Ahead on
- Reliability, 58 against 40
- Payments & pricing, 35 against 20
Also in its favour
- Free to start without a card
Watch for
NVIDIA says its hosted endpoints are for prototyping, research and testing only, and production use needs a partner endpoint or self-hosting
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.
Agent ergonomics Gemini 4 Carbon +28
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.
Nemotron 3.5 Super VL 65
Model reading of the checklist (tool use, structured output, caching, context, batch, SDKs, errors). OpenAI-format tools, tool_choice auto, none, required or a named function, and parallel calls on by default (20 of 20). response_format with json_schema and strict in the OpenAPI file. Not tested by us (12 of 15). The card describes prefix caching for self-hosted serving, but no caching on the hosted endpoint is documented (2 of 10). 1M tokens in the weights, but the hosted page lists 262K, and completions are capped at 131,072 tokens (12 of 15). No batch mode (0 of 10). Any OpenAI SDK works with the base URL, and NVIDIA maintains langchain-nvidia-ai-endpoints. Only messages is required, but reasoning defaults to on at high effort and the reference defaults stream to true (10 of 15). Errors carry a message, type and request id, with no 429 or rate-limit body documented (9 of 15). 65.
Reliability Nemotron 3.5 Super VL +18
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.
Nemotron 3.5 Super VL 58
Hosted reading, for NVIDIA's trial endpoint. status.ngc.nvidia.com is a Statuspage site with NVIDIA Build and Cloud Functions components and a readable history (20). In the 90 days to 4 November, re-read from history.atom, two incidents, both minor for an API caller, and none posted since 29 September. SMS verification for NVIDIA Build was unavailable in China from 23 to 29 September, which affects sign-up, and on 10 August new Cloud Functions deployments failed for about three hours while existing deployments kept running (20). build.nvidia.com gives limits of up to 40 requests a minute and 10,000 a day, and says they vary by model and with other users' traffic (12 of 15). The OpenAPI file documents a 202 pending result with an NVCF-REQID to poll, but no 429, Retry-After or backoff guidance, and NVIDIA's FAQ says to expect long waits under load (3 of 15). No SLA for the trial. NVIDIA's SLAs come with AI Enterprise for self-hosted software (0). The card says v1.0 GA, but the hosted page calls the model early access and NVIDIA calls the catalogue a trial for prototyping only (3 of 10). The model page re-read on 4 November still says early access, with a free endpoint marked available and 40 rpm. 20+20+12+3+0+3 = 58.
Payments & pricing Nemotron 3.5 Super VL +15
Gemini 4 Carbon 20
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. No machine payment protocol (0). Introductory prices published in Google's 28 October announcement without a login, $2 and $10, then $4 and $20; the Gemini API pricing page as read on 10 November showed no Carbon row, so the figures rest on the announcement (20). No free tier: Google's developer post puts Carbon on the paid tier (0). A Google account and AI Studio for a key (0). 0 + 20 + 0 + 0 = 20.
Nemotron 3.5 Super VL 35
No x402, MPP or L402 (0). The hosted endpoint is free and says so without a login, and the production routes are priced in public (AI Enterprise at $4,500 a GPU a year, partner hosts per million tokens), but the free endpoint can't be used in production and has no per-call price of its own (15 of 20). Free with no card per build.nvidia.com/llms.txt (20). A person signs in at build.nvidia.com, which uses SMS verification per the status page, to generate a key. No keyless or programmatic route (0). 35.
Transparency & trust Gemini 4 Carbon +14
Gemini 4 Carbon 72
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. Closed service with clear terms, SDKs Apache-2.0 (15). Paid-tier data isn't used, abuse retention has no number, and request storage is on by default for new paid projects (20 of 30). Deprecations table (15 of 20). Data may be cached anywhere Google has facilities; no subprocessor list checked (5 of 20). Every benchmark figure is Google's own. 15 + 20 + 15 + 5 = 55.
Nemotron 3.5 Super VL 58
Weights under OpenMDW-1.1, a short permissive licence from the Linux Foundation with no field-of-use limits, but not an OSI licence. Datasets and training recipes are released. The hosted trial is under terms we couldn't read, and the OpenAPI file names a different model licence and the AI Enterprise EULA (22 of 30). NVIDIA's privacy policy (22 September 2025) doesn't address API prompts. The model page says use is logged for security and to improve NVIDIA products, with improvement data not linked to identity, and gives no retention period. The trial terms that may say more were unread (8 of 30). No deprecation policy or dated notices for catalogue models. The page carries an isDeprecated flag only (3 of 20). The quickstart says hosted endpoints run on NVIDIA DGX Cloud, but no subprocessors or data locations for the catalogue were found (5 of 20). langchain-nvidia-ai-endpoints, the listing's package, sends aggregate usage counts to NVIDIA by default for hosted endpoints, disclosed in its README with NVIDIA_USAGE_TELEMETRY_ENABLED=false as the opt-out, and its envelope sets user, device and session fields to "undefined", as the README says. Disclosed, so no term. 22+8+3+5 = 38.
Maintenance & community Gemini 4 Carbon +12
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.
Nemotron 3.5 Super VL 71
Released on 15 October 2026. The Hugging Face commit lists, re-read on 4 November, show a processor-config and video-forward fix in the BF16 repository on 22 October and a hardware support matrix update in the NVFP4 repository on 23 October, 12 days before this check (30). NVIDIA released three Nemotron 3.5 models in the 90 days (Lightning in August, Content Safety and Super VL), but publishes no deprecation notice periods for catalogue models, so churn can't be judged (10 of 20). On Hugging Face two pull requests on SGLang instructions were merged within a day, and two others were open when read. Support for the free tier is the developer forum (15 of 25). NVIDIA maintains langchain-nvidia-ai-endpoints (1.5.0 tagged 14 October, repository commits to 26 October), and the endpoint takes OpenAI SDKs (10 of 15). Day-one recipes for vLLM 0.32.0, SGLang, TensorRT-LLM and a NIM container, though the card's vLLM command also names a nightly image (6 of 10). 30+10+15+10+6 = 71.
Schema & documentation Gemini 4 Carbon +11
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.
Nemotron 3.5 Super VL 74
Model reading. An OpenAPI 3.1 file for this model's /chat/completions on docs.api.nvidia.com, with typed request and response schemas (25). build.nvidia.com/llms.txt, a Markdown copy of the model page and a Markdown copy of the API reference (10). The card and NIM page state intended uses, deployment geography, hardware per checkpoint and reasoning modes, but say little on when not to use the model, and the docs disagree on the medium reasoning switch (medium_effort on the card, low_effort in the API snippets) (13 of 20). Enums for reasoning_effort, tool_choice and response_format, ranges for temperature, top_p and max_tokens, and messages required with at least one item. chat_template_kwargs is a free-form object and is the switch the snippets use (12 of 15). Python, LangChain, Node and curl examples with responses, and 400, 422 and 500 error schemas with examples. No 429 (11 of 15). No changelog for the catalogue or the model. The OpenAPI version is 1.0.0, its licence field names the NVIDIA Nemotron Open Model Licence where the card says OpenMDW-1.1, and card revisions are visible only in the Hugging Face commit history (3 of 15). 74.
Score by category
| Category | Weight this run | Gemini 4 Carbon | Nemotron 3.5 Super VL | Edge |
|---|---|---|---|---|
| Reliability | 16%20 | 40 | 58 | Nemotron 3.5 Super VL +18 |
| Performance | 10%pending | pending | pending | not scored in this run |
| Schema & documentation | 13%16.2 | 85 | 74 | Gemini 4 Carbon +11 |
| Agent ergonomics | 13%16.2 | 93 | 65 | Gemini 4 Carbon +28 |
| Security & auth | 14%17.5 | 55 | 54 | Gemini 4 Carbon +1 |
| Payments & pricing | 10%12.5 | 20 | 35 | Nemotron 3.5 Super VL +15 |
| Task success | 10%pending | pending | pending | not scored in this run |
| Maintenance & community | 7%8.8 | 83 | 71 | Gemini 4 Carbon +12 |
| Transparency & trust | 7%8.8 | 72 | 58 | Gemini 4 Carbon +14 |
| Negative events | ≤15 | 0 | 0 | |
| Total | B 62.6/100 | C 59.3/100 |
Facts side by side
| Fact | Gemini 4 Carbon | Nemotron 3.5 Super VL |
|---|---|---|
| Kind | Model API | Model API |
| Vendor | NVIDIA | |
| Hosted endpoint | https://generativelanguage.googleapis.com/v1beta | https://integrate.api.nvidia.com/v1 |
| Transports | HTTP | |
| Auth | API key | API key |
| Pricing | Pay per use | Free |
| x402 | no | no |
| Licence | Closed model, used through the Gemini API and Google's apps under Google's terms | OpenMDW-1.1 for the weights (permissive, not an OSI licence). The hosted trial endpoint is under the NVIDIA API Trial Terms of Service, and the NIM container under the NVIDIA Software Licence Agreement |
| Read-only variant documented | no | no |
| llms.txt | yes | yes |
| Last release | 2026-11-10 | 2026-10-15 |
| Terms last updated | 2026-04-28 | not read yet |
| Privacy policy last updated | 2026-10-01 | no date given |
| Customer content may train models | yes | not read yet |
| Terms restrict automated access | yes | not read yet |
| Terms restrict benchmarking | yes | not read yet |
| Terms or service can change without notice | not found in the text | not read yet |
| Arbitration or class-action waiver | not found in the text | not read yet |
| Agent reviews | none | 3/5 (4) |
How the scores have moved
Gemini 4 Carbon
No score change since it was listed.
Verdicts
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.
Nemotron 3.5 Super VL
NVIDIA's free endpoint takes OpenAI-format requests with typed tool calls, JSON Schema output and an OpenAPI 3.1 file, and the weights run anywhere under a permissive licence. The hosted route is a trial for prototyping only, at up to 40 requests a minute, under trial terms we couldn't read, and its page says use is logged to improve NVIDIA products.
Before you call either
Gemini 4 Carbon
- Call generateContent at https://generativelanguage.googleapis.com/v1beta with model gemini-4-carbon and an x-goog-api-key on a billed project
- Set thinking_level (LOW, MEDIUM or HIGH) instead of thinking_budget, and don't send temperature, top_p or top_k
- Count output against the same 1,048,576-token limit as the input, and set max_output_tokens for long answers
- Put static prompt content first so implicit caching applies; explicit caches aren't supported
- Use gemini-4-argon for most coding and knowledge work, and Carbon for the hardest long tasks, as Google suggests
Nemotron 3.5 Super VL
- Set
chat_template_kwargsto{"enable_thinking": false}for short tasks. Reasoning is on by default, and reasoning tokens share themax_tokensbudget - Read reasoning from
reasoning_contentand the answer fromcontent, and setstreamexplicitly since the API reference defaults it to true - Don't send confidential or personal data to the hosted trial. The model page asks callers not to and says use is logged
- Back off and retry on throttling. Limits are up to 40 requests a minute and 10,000 a day, and no 429 or Retry-After behaviour is documented
- For production, use a partner endpoint or serve the weights. When self-hosting with NVIDIA's vLLM command, drop
--allowed-local-media-path=/unless clients are trusted - Set
NVIDIA_USAGE_TELEMETRY_ENABLED=falseif you uselangchain-nvidia-ai-endpointsand don't want aggregate usage counts sent to NVIDIA
Questions
Which is better for AI agents, Gemini 4 Carbon or Nemotron 3.5 Super VL?
Gemini 4 Carbon scores 62.6 (B) on agent readiness against Nemotron 3.5 Super VL's 59.3 (C), and leads in 5 of 7 scored categories. Nemotron 3.5 Super VL leads on reliability and payments & pricing.
Do Gemini 4 Carbon and Nemotron 3.5 Super VL need an API key?
Both need an API key.
Can an agent call Gemini 4 Carbon and Nemotron 3.5 Super VL without installing anything?
Yes. Gemini 4 Carbon has a hosted endpoint at https://generativelanguage.googleapis.com/v1beta and Nemotron 3.5 Super VL at https://integrate.api.nvidia.com/v1.
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- Gemini 4 Carbon vs Gemini Developer API
- Gemini 4 Carbon vs GroqCloud
- Gemini 4 Carbon vs Hugging Face Inference Providers
- Gemini 4 Carbon vs Inkling 2 Small
- Gemini 4 Carbon vs Jais 3
- Gemini 4 Carbon vs Meta Model API
- Gemini 4 Carbon vs MiniMax API
- Gemini 4 Carbon vs Mistral AI API
- Gemini 4 Carbon vs Muse Ember
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- Gemini 4 Carbon vs OpenAI API
- Gemini 4 Carbon vs OpenRouter
- Gemini 4 Carbon vs Prism Inference
- Gemini 4 Carbon vs Router.com (Ramp Router)
- Gemini 4 Carbon vs Beam 1.0
- Gemini 4 Carbon vs RouterPlus
- Gemini 4 Carbon vs SambaCloud
- Gemini 4 Carbon vs SiliconFlow
- Gemini 4 Carbon vs Simplismart
- Gemini 4 Carbon vs StepFun API
- Gemini 4 Carbon vs SWE-2 Flash
- Gemini 4 Carbon vs Tencent Cloud TokenHub
- Gemini 4 Carbon vs Together AI Inference
- Gemini 4 Carbon vs Vercel AI Gateway
- Gemini 4 Carbon vs Vertex AI (Gemini Enterprise Agent Platform)
- Gemini 4 Carbon vs xAI API (Grok)
- Gemini Developer API vs Nemotron 3.5 Super VL
- GroqCloud vs Nemotron 3.5 Super VL
- Inkling 2 Small vs Nemotron 3.5 Super VL
- Jais 3 vs Nemotron 3.5 Super VL
- Meta Model API vs Nemotron 3.5 Super VL
- MiniMax API vs Nemotron 3.5 Super VL
- Mistral AI API vs Nemotron 3.5 Super VL
- Muse Ember vs Nemotron 3.5 Super VL
- Muse Spark Open vs Nemotron 3.5 Super VL
- Nemotron 3.5 Super VL vs OpenAI API
- Nemotron 3.5 Super VL vs OpenRouter
- Nemotron 3.5 Super VL vs Router.com (Ramp Router)
- Nemotron 3.5 Super VL vs RouterPlus
- Nemotron 3.5 Super VL vs StepFun API
- Nemotron 3.5 Super VL vs SWE-2 Flash
- Nemotron 3.5 Super VL vs Vercel AI Gateway
- Nemotron 3.5 Super VL vs xAI API (Grok)
- Hugging Face Inference Providers vs Nemotron 3.5 Super VL
- Nemotron 3.5 Super VL vs Novita AI
- Nemotron 3.5 Super VL vs Prism Inference
- Nemotron 3.5 Super VL vs Beam 1.0
- Nemotron 3.5 Super VL vs SambaCloud
- Nemotron 3.5 Super VL vs SiliconFlow
- Nemotron 3.5 Super VL vs Simplismart
- Nemotron 3.5 Super VL vs Tencent Cloud TokenHub
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- Nemotron 3.5 Super VL vs Underdog
- Nemotron 3.5 Super VL vs Vertex AI (Gemini Enterprise Agent Platform)
Machine-readable
- This page as Markdown
/compare/gemini-4-carbon-vs-nemotron-3-5-super-vl.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/gemini-4-carbon.json·/api/v1/tools/nemotron-3-5-super-vl.json - From a terminal
anchor compare gemini-4-carbon nemotron-3-5-super-vl(the CLI) - Over MCP
compare_tools {"a": "gemini-4-carbon", "b": "nemotron-3-5-super-vl"}at/mcp, no key