Llama 3.1 8B Instruct vs Mistral NeMo (2407)
Llama 3.1 8B Instruct (2024) and Mistral NeMo (2407) (2024) are compact production models from AI at Meta and MistralAI. Llama 3.1 8B Instruct ships a 128K-token context window, while Mistral NeMo (2407) ships a 128K-token context window. On pricing, Llama 3.1 8B Instruct costs $0.02/1M input tokens versus $0.02/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Llama 3.1 8B Instruct is safer overall; choose Mistral NeMo (2407) when provider fit matters.
Decision scorecard
Local evidence first| Signal | Llama 3.1 8B Instruct | Mistral NeMo (2407) |
|---|---|---|
| Decision fit | RAG, Long context, and Classification | Long context |
| Context window | 128K | 128K |
| Cheapest output | $0.05/1M tokens | $0.03/1M tokens |
| Provider routes | 12 tracked | 5 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Llama 3.1 8B Instruct has broader tracked provider coverage for fallback and procurement flexibility.
- Llama 3.1 8B Instruct uniquely exposes Structured outputs in local model data.
- Local decision data tags Llama 3.1 8B Instruct for RAG, Long context, and Classification.
- Mistral NeMo (2407) has the lower cheapest tracked output price at $0.03/1M tokens.
- Local decision data tags Mistral NeMo (2407) for Long context.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output prices on this page.
Llama 3.1 8B Instruct
$28.50
Cheapest tracked route: OpenRouter
Mistral NeMo (2407)
$23.50
Cheapest tracked route: OpenRouter
Estimated monthly gap: $5.00. Batch, cache, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on OpenRouter and Fireworks AI; start route-level A/B tests there.
- Mistral NeMo (2407) is $0.02/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Structured outputs before moving production traffic.
- Provider overlap exists on Fireworks AI and OpenRouter; start route-level A/B tests there.
- Llama 3.1 8B Instruct is $0.02/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Llama 3.1 8B Instruct adds Structured outputs in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-07-23 | 2024-07-18 |
| Context window | 128K | 128K |
| Parameters | 8B | 12B |
| Architecture | decoder only | decoder only |
| License | Open Source | Apache 2.0 |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Llama 3.1 8B Instruct | Mistral NeMo (2407) |
|---|---|---|
| Input price | $0.02/1M tokens | $0.02/1M tokens |
| Output price | $0.05/1M tokens | $0.03/1M tokens |
| Providers |
Capabilities
| Capability | Llama 3.1 8B Instruct | Mistral NeMo (2407) |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | Yes | No |
| Code execution | No | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on structured outputs: Llama 3.1 8B Instruct. 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.
For cost, Llama 3.1 8B Instruct lists $0.02/1M input and $0.05/1M output tokens, while Mistral NeMo (2407) lists $0.02/1M input and $0.03/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Mistral NeMo (2407) lower by about $0.01 per million blended tokens. Availability is 12 providers versus 5, so concentration risk also matters.
Choose Llama 3.1 8B Instruct when provider fit and broader provider choice are central to the workload. Choose Mistral NeMo (2407) when provider fit 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. For teams standardizing a stack, that distinction is often the difference between a benchmark win and a reliable deployment.
FAQ
Which has a larger context window, Llama 3.1 8B Instruct or Mistral NeMo (2407)?
Llama 3.1 8B Instruct supports 128K tokens, while Mistral NeMo (2407) supports 128K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Which is cheaper, Llama 3.1 8B Instruct or Mistral NeMo (2407)?
Llama 3.1 8B Instruct is cheaper on tracked token pricing. Llama 3.1 8B Instruct costs $0.02/1M input and $0.05/1M output tokens. Mistral NeMo (2407) costs $0.02/1M input and $0.03/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Llama 3.1 8B Instruct or Mistral NeMo (2407) open source?
Llama 3.1 8B Instruct is listed under Open Source. Mistral NeMo (2407) 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 structured outputs, Llama 3.1 8B Instruct or Mistral NeMo (2407)?
Llama 3.1 8B Instruct has the clearer documented structured outputs signal in this comparison. If structured outputs 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 8B Instruct and Mistral NeMo (2407)?
Llama 3.1 8B Instruct is available on OctoAI API (Deprecated), Together AI, Fireworks AI, NVIDIA NIM, and GroqCloud. Mistral NeMo (2407) is available on Mistral AI Studio, OpenRouter, Fireworks AI, Bitdeer AI, and SiliconFlow. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Llama 3.1 8B Instruct over Mistral NeMo (2407)?
Llama 3.1 8B Instruct is safer overall; choose Mistral NeMo (2407) when provider fit matters. If your workload also depends on provider fit, start with Llama 3.1 8B Instruct; if it depends on provider fit, run the same evaluation with Mistral NeMo (2407).
Continue comparing
Last reviewed: 2026-05-16. Data sourced from public model cards and provider documentation.