Mistral Large vs Mixtral 8x7B
Mistral Large (2024) and Mixtral 8x7B (2023) are compact production models from MistralAI. Mistral Large ships a 32k-token context window, while Mixtral 8x7B ships a 32k-token context window. On pricing, Mixtral 8x7B costs $0.15/1M input tokens versus $0.32/1M for the alternative. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
Mixtral 8x7B is ~113% cheaper at $0.15/1M; pay for Mistral Large only for vision-heavy evaluation.
Decision scorecard
Local evidence first| Signal | Mistral Large | Mixtral 8x7B |
|---|---|---|
| Best for | multimodal apps, tool-calling agents, and provider-routed production | provider-routed production |
| Decision fit | Agents, Vision, and Classification | Coding and Classification |
| Context window | 32k | 32k |
| Cheapest output | $0.96/1M tokens | $0.45/1M tokens |
| Provider routes | 8 tracked | 18 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- 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.
- Mixtral 8x7B has the lower cheapest tracked output price at $0.45/1M tokens.
- Mixtral 8x7B has broader tracked provider coverage for fallback and procurement flexibility.
- Local decision data tags Mixtral 8x7B for Coding 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
Mixtral 8x7B
$233
Cheapest tracked route/tier: Mistral AI Studio
Estimated monthly gap: $264. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on NVIDIA NIM, GCP Vertex AI, and AWS Bedrock; start route-level A/B tests there.
- Mixtral 8x7B is $0.51/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Vision, Function calling, and Tool use before moving production traffic.
- Provider overlap exists on NVIDIA NIM, Microsoft Foundry, and AWS Bedrock; start route-level A/B tests there.
- Mistral Large is $0.51/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Mistral Large adds Vision, Function calling, and Tool use in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-02-08 | 2023-12-11 |
| Context window | 32k | 32k |
| Parameters | 123B | 8x7B |
| Architecture | - | mixture of experts |
| License | Mistral License | Apache 2.0(OSI) |
| Openness | Open weights | Open source |
| Commercial use | Non-commercial only | Commercial use allowed |
| Knowledge cutoff | 2024-03 | 2023-12 |
Pricing and availability
| Pricing attribute | Mistral Large | Mixtral 8x7B |
|---|---|---|
| Input price | $0.32/1M tokens | $0.15/1M tokens |
| Output price | $0.96/1M tokens | $0.45/1M tokens |
| Providers |
Capabilities
| Capability | Mistral Large | Mixtral 8x7B |
|---|---|---|
| Vision | Yes | No |
| Multimodal | No | No |
| 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
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on vision: Mistral Large, 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.
For cost, Mistral Large lists $0.32/1M input and $0.96/1M output tokens on the cheapest tracked provider, while Mixtral 8x7B lists $0.15/1M input and $0.45/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Mixtral 8x7B lower by about $0.27 per million blended tokens. Availability is 8 providers versus 18, so concentration risk also matters.
Choose Mistral Large when vision-heavy evaluation are central to the workload. Choose Mixtral 8x7B when provider fit, lower input-token cost, and broader provider choice 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, Mistral Large or Mixtral 8x7B?
Mistral Large supports 32k tokens, while Mixtral 8x7B supports 32k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Which is cheaper, Mistral Large or Mixtral 8x7B?
Mixtral 8x7B is cheaper on tracked token pricing. Mistral Large costs $0.32/1M input and $0.96/1M output tokens. Mixtral 8x7B costs $0.15/1M input and $0.45/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Mistral Large or Mixtral 8x7B open source?
Mistral Large is listed under Mistral License. Mixtral 8x7B 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 vision, Mistral Large or Mixtral 8x7B?
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 function calling, Mistral Large or Mixtral 8x7B?
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 Mixtral 8x7B?
Mistral Large is available on NVIDIA NIM, Microsoft Foundry, AWS Bedrock, Mistral AI Studio, and IBM watsonx. Mixtral 8x7B is available on Databricks Foundation Model Serving, NVIDIA NIM, GCP Vertex AI, AWS Bedrock, and OctoAI API (Deprecated). 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.