Llama 3.1 Nemotron Nano 4B v1.1 vs Mistral 7B v0.3
Llama 3.1 Nemotron Nano 4B v1.1 (2025) and Mistral 7B v0.3 (2024) are compact production models from NVIDIA AI and MistralAI. Llama 3.1 Nemotron Nano 4B v1.1 ships a 4K-token context window, while Mistral 7B v0.3 ships a 32K-token context window. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing.
Mistral 7B v0.3 fits 8x more tokens; pick it for long-context work and Llama 3.1 Nemotron Nano 4B v1.1 for tighter calls.
Decision scorecard
Local evidence first| Signal | Llama 3.1 Nemotron Nano 4B v1.1 | Mistral 7B v0.3 |
|---|---|---|
| Decision fit | General | Agents and JSON / Tool use |
| Context window | 4K | 32K |
| Cheapest output | - | - |
| Provider routes | 1 tracked | 0 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Llama 3.1 Nemotron Nano 4B v1.1 has broader tracked provider coverage for fallback and procurement flexibility.
- Mistral 7B v0.3 has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Mistral 7B v0.3 uniquely exposes Function calling in local model data.
- Local decision data tags Mistral 7B v0.3 for Agents and JSON / Tool use.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output prices on this page.
Llama 3.1 Nemotron Nano 4B v1.1
Unavailable
No complete token price in local provider data
Mistral 7B v0.3
Unavailable
No complete token price in local provider data
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- No overlapping tracked provider route is sourced for Llama 3.1 Nemotron Nano 4B v1.1 and Mistral 7B v0.3; plan for SDK, billing, or endpoint changes.
- Mistral 7B v0.3 adds Function calling in local capability data.
- No overlapping tracked provider route is sourced for Mistral 7B v0.3 and Llama 3.1 Nemotron Nano 4B v1.1; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Function calling before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-04-01 | 2024-05-23 |
| Context window | 4K | 32K |
| Parameters | 4B | 7B |
| Architecture | decoder only | decoder only |
| License | 1 | Apache 2.0 |
| Knowledge cutoff | - | 2023-12 |
Pricing and availability
| Pricing attribute | Llama 3.1 Nemotron Nano 4B v1.1 | Mistral 7B v0.3 |
|---|---|---|
| Input price | - | - |
| Output price | - | - |
| Providers | - |
Pricing not yet sourced for either model.
Capabilities
| Capability | Llama 3.1 Nemotron Nano 4B v1.1 | Mistral 7B v0.3 |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | No | Yes |
| Tool use | No | No |
| Structured outputs | No | No |
| Code execution | No | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on function calling: Mistral 7B v0.3. 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: Llama 3.1 Nemotron Nano 4B v1.1 has no token price sourced yet and Mistral 7B v0.3 has no token price sourced yet. Provider availability is 1 tracked routes versus 0. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose Llama 3.1 Nemotron Nano 4B v1.1 when provider fit and broader provider choice are central to the workload. Choose Mistral 7B v0.3 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. This keeps the decision grounded in measurable tradeoffs instead of brand-level assumptions. It also helps separate model capability from provider packaging, which can change cost and latency.
FAQ
Which has a larger context window, Llama 3.1 Nemotron Nano 4B v1.1 or Mistral 7B v0.3?
Mistral 7B v0.3 supports 32K tokens, while Llama 3.1 Nemotron Nano 4B v1.1 supports 4K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Is Llama 3.1 Nemotron Nano 4B v1.1 or Mistral 7B v0.3 open source?
Llama 3.1 Nemotron Nano 4B v1.1 is listed under 1. Mistral 7B v0.3 is listed under Apache 2.0. 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 function calling, Llama 3.1 Nemotron Nano 4B v1.1 or Mistral 7B v0.3?
Mistral 7B v0.3 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 Llama 3.1 Nemotron Nano 4B v1.1 and Mistral 7B v0.3?
Llama 3.1 Nemotron Nano 4B v1.1 is available on NVIDIA NIM. Mistral 7B v0.3 is available on the tracked providers still being sourced. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Llama 3.1 Nemotron Nano 4B v1.1 over Mistral 7B v0.3?
Mistral 7B v0.3 fits 8x more tokens; pick it for long-context work and Llama 3.1 Nemotron Nano 4B v1.1 for tighter calls. If your workload also depends on provider fit, start with Llama 3.1 Nemotron Nano 4B v1.1; if it depends on long-context analysis, run the same evaluation with Mistral 7B v0.3.
Continue comparing
Last reviewed: 2026-05-19. Data sourced from public model cards and provider documentation.