Llama 3.1 405B Instruct vs Mistral NeMo Instruct (2407)
Llama 3.1 405B Instruct (2024) and Mistral NeMo Instruct (2407) (2024) are compact production models from AI at Meta and MistralAI. Llama 3.1 405B Instruct ships a 128k-token context window, while Mistral NeMo Instruct (2407) ships a 128k-token context window. On Massive Multitask Language Understanding, Llama 3.1 405B Instruct leads by 7.1 pts. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
Mistral NeMo Instruct (2407) is ~11900% cheaper at $0.02/1M; pay for Llama 3.1 405B Instruct only for provider fit.
Decision scorecard
Local evidence first| Signal | Llama 3.1 405B Instruct | Mistral NeMo Instruct (2407) |
|---|---|---|
| Best for | provider-routed production | provider-routed production |
| Decision fit | RAG, Long context, and Classification | Coding, Long context, and Classification |
| Context window | 128k | 128k |
| Cheapest output | $2.40/1M tokens | $0.04/1M tokens |
| Provider routes | 11 tracked | 7 tracked |
| Shared benchmarks | Massive Multitask Language Understanding leader | 1 shared |
Decision tradeoffs
- Llama 3.1 405B Instruct holds a shared-benchmark lead on Massive Multitask Language Understanding, ahead by 7.1 points.
- Llama 3.1 405B Instruct has broader tracked provider coverage for fallback and route flexibility.
- Llama 3.1 405B Instruct uniquely exposes Structured outputs in local model data.
- Local decision data tags Llama 3.1 405B Instruct for RAG, Long context, and Classification.
- Mistral NeMo Instruct (2407) has the lower cheapest tracked output price at $0.04/1M tokens.
- Local decision data tags Mistral NeMo Instruct (2407) for Coding, Long context, and Classification.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
Llama 3.1 405B Instruct
$2,520
Cheapest tracked route/tier: AWS Bedrock
Mistral NeMo Instruct (2407)
$26.00
Cheapest tracked route/tier: DeepInfra
Estimated monthly gap: $2,494. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on NVIDIA NIM, Microsoft Foundry, and Fireworks AI; start route-level A/B tests there.
- Mistral NeMo Instruct (2407) is $2.36/1M 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, NVIDIA NIM, and Microsoft Foundry; start route-level A/B tests there.
- Llama 3.1 405B Instruct is $2.36/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Llama 3.1 405B Instruct adds Structured outputs in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-07-23 | 2024-07-18 |
| Context window | 128k | 128k |
| Parameters | 405B | 12B |
| Architecture | Decoder Only | Decoder Only |
| License | Llama 3 Community | Apache 2.0OSI-approved |
| Openness | Open weights | Open source |
| Weights | Available | Unknown |
| Code | Unknown | Unknown |
| Commercial use | Commercial use: conditional | Commercial use: permitted |
| Knowledge cutoff | 2023-12 | 2024-04 |
Pricing and availability
| Pricing attribute | Llama 3.1 405B Instruct | Mistral NeMo Instruct (2407) |
|---|---|---|
| Input price | $2.40/1M tokens | $0.02/1M tokens |
| Output price | $2.40/1M tokens | $0.04/1M tokens |
| Providers |
Capabilities
| Capability | Llama 3.1 405B Instruct | Mistral NeMo Instruct (2407) |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| JSON / Tool use | No | No |
| Structured outputs | Yes | No |
| Code execution | No | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
| Benchmark | Llama 3.1 405B Instruct | Mistral NeMo Instruct (2407) |
|---|---|---|
| Massive Multitask Language Understanding | 88.6 | 81.5 |
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Last reviewed: 2026-07-11. Data sourced from public model cards and provider documentation.