Mistral Large vs Nemotron 3 Nano Omni
Mistral Large (2024) and Nemotron 3 Nano Omni (2026) are compact production models from MistralAI and NVIDIA AI. Mistral Large ships a 32k-token context window, while Nemotron 3 Nano Omni ships a 262k-token context window. On MMLU PRO, Nemotron 3 Nano Omni leads by 20.3 pts. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
Nemotron 3 Nano Omni fits 8x more tokens; pick it for long-context work and Mistral Large for tighter calls.
Decision scorecard
Local evidence first| Signal | Mistral Large | Nemotron 3 Nano Omni |
|---|---|---|
| Best for | multimodal apps, tool-calling agents, and provider-routed production | multimodal apps |
| Decision fit | Agents, Vision, and Classification | Long context, Vision, and Classification |
| Context window | 32k | 262k |
| Cheapest output | $0.96/1M tokens | - |
| Provider routes | 8 tracked | 1 tracked |
| Shared benchmarks | 1 rows | MMLU PRO leader |
Decision tradeoffs
- Mistral Large has broader tracked provider coverage for fallback and procurement flexibility.
- Mistral Large uniquely exposes Vision, Function calling, and Tool use in local model data.
- Local decision data tags Mistral Large for Agents, Vision, and Classification.
- Nemotron 3 Nano Omni leads the largest shared benchmark signal on MMLU PRO by 20.3 points.
- Nemotron 3 Nano Omni has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Nemotron 3 Nano Omni uniquely exposes Multimodal in local model data.
- Local decision data tags Nemotron 3 Nano Omni for Long context, Vision, and Classification.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
Mistral Large
$496
Cheapest tracked route/tier: GCP Vertex AI
Nemotron 3 Nano Omni
Unavailable
No complete token price in local provider data
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- Provider overlap exists on OpenRouter; start route-level A/B tests there.
- Check replacement coverage for Vision, Function calling, and Tool use before moving production traffic.
- Nemotron 3 Nano Omni adds Multimodal in local capability data.
- Provider overlap exists on OpenRouter; start route-level A/B tests there.
- Check replacement coverage for Multimodal before moving production traffic.
- Mistral Large adds Vision, Function calling, and Tool use in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-02-08 | 2026-04-28 |
| Context window | 32k | 262k |
| Parameters | 123B | 30B |
| Architecture | - | Hybrid Mamba-Transformer MoE |
| License | Proprietary | Open Source |
| Knowledge cutoff | 2024-03 | - |
Pricing and availability
| Pricing attribute | Mistral Large | Nemotron 3 Nano Omni |
|---|---|---|
| Input price | $0.32/1M tokens | - |
| Output price | $0.96/1M tokens | - |
| Providers |
Capabilities
| Capability | Mistral Large | Nemotron 3 Nano Omni |
|---|---|---|
| Vision | Yes | No |
| Multimodal | No | Yes |
| Reasoning | No | No |
| Function calling | Yes | No |
| Tool use | Yes | No |
| Structured outputs | Yes | No |
| Code execution | No | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
| Benchmark | Mistral Large | Nemotron 3 Nano Omni |
|---|---|---|
| MMLU PRO | 51.5 | 71.8 |
Deep dive
On shared benchmark coverage, MMLU PRO has Mistral Large at 51.5 and Nemotron 3 Nano Omni at 71.8, with Nemotron 3 Nano Omni ahead by 20.3 points. The largest visible gap is 20.3 points on MMLU PRO, which matters most when that benchmark mirrors your workload. Treat isolated benchmark wins as directional, because provider routing, prompt style, and tool access can move real application results.
The capability footprint differs most on vision: Mistral Large, multimodal input: Nemotron 3 Nano Omni, function calling: Mistral Large, tool use: Mistral Large, and structured outputs: Mistral Large. Both models share the core language-model surface, so the practical split is not just feature count. Use those differences to decide whether the page is about raw model quality, agentic coding support, multimodal ingestion, or predictable structured API behavior.
Pricing coverage is uneven: Mistral Large has $0.32/1M input tokens and Nemotron 3 Nano Omni has no token price sourced yet. Provider availability is 8 tracked routes versus 1. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose Mistral Large when vision-heavy evaluation and broader provider choice are central to the workload. Choose Nemotron 3 Nano Omni when long-context analysis and larger context windows are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship.
FAQ
Which has a larger context window, Mistral Large or Nemotron 3 Nano Omni?
Nemotron 3 Nano Omni supports 262k tokens, while Mistral Large supports 32k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Is Mistral Large or Nemotron 3 Nano Omni open source?
Mistral Large is listed under Proprietary. Nemotron 3 Nano Omni is listed under Open Source. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.
Which is better for vision, Mistral Large or Nemotron 3 Nano Omni?
Mistral Large has the clearer documented vision signal in this comparison. If vision is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is better for multimodal input, Mistral Large or Nemotron 3 Nano Omni?
Nemotron 3 Nano Omni has the clearer documented multimodal input signal in this comparison. If multimodal input is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for function calling, Mistral Large or Nemotron 3 Nano Omni?
Mistral Large has the clearer documented function calling signal in this comparison. If function calling is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run Mistral Large and Nemotron 3 Nano Omni?
Mistral Large is available on NVIDIA NIM, Microsoft Foundry, AWS Bedrock, Mistral AI Studio, and IBM watsonx. Nemotron 3 Nano Omni is available on OpenRouter. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
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Last reviewed: 2026-05-22. Data sourced from public model cards and provider documentation.