Mistral Large vs Phi 3.5 Mini Instruct
Mistral Large (2024) and Phi 3.5 Mini Instruct (2024) are compact production models from MistralAI and Microsoft Research. Mistral Large ships a 32k-token context window, while Phi 3.5 Mini Instruct ships a 128k-token context window. On pricing, Mistral Large costs $0.32/1M input tokens versus $0.90/1M 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.
Mistral Large is ~181% cheaper at $0.32/1M; pay for Phi 3.5 Mini Instruct only for long-context analysis.
Decision scorecard
Local evidence first| Signal | Mistral Large | Phi 3.5 Mini Instruct |
|---|---|---|
| Best for | multimodal apps, tool-calling agents, and provider-routed production | provider-routed production |
| Decision fit | Agents, Vision, and Classification | Long context |
| Context window | 32k | 128k |
| Cheapest output | $0.96/1M tokens | $0.90/1M tokens |
| Provider routes | 8 tracked | 2 tracked |
| Shared benchmarks | 0 rows | 0 rows |
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.
- Phi 3.5 Mini Instruct has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Phi 3.5 Mini Instruct has the lower cheapest tracked output price at $0.90/1M tokens.
- Local decision data tags Phi 3.5 Mini Instruct for Long context.
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
Phi 3.5 Mini Instruct
$945
Cheapest tracked route/tier: Fireworks AI
Estimated monthly gap: $449. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on Fireworks AI and NVIDIA NIM; start route-level A/B tests there.
- Phi 3.5 Mini Instruct is $0.06/1M 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 and Fireworks AI; start route-level A/B tests there.
- Mistral Large is $0.06/1M 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 | 2024-08-20 |
| Context window | 32k | 128k |
| Parameters | 123B | 3.8B |
| Architecture | - | decoder only |
| License | Mistral License | MIT(OSI) |
| Openness | Open weights | Open source |
| Commercial use | Non-commercial only | Commercial use allowed |
| Knowledge cutoff | 2024-03 | 2023-10 |
Pricing and availability
| Pricing attribute | Mistral Large | Phi 3.5 Mini Instruct |
|---|---|---|
| Input price | $0.32/1M tokens | $0.90/1M tokens |
| Output price | $0.96/1M tokens | $0.90/1M tokens |
| Providers |
Capabilities
| Capability | Mistral Large | Phi 3.5 Mini Instruct |
|---|---|---|
| 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 Phi 3.5 Mini Instruct lists $0.90/1M input and $0.90/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Mistral Large lower by about $0.39 per million blended tokens. Availability is 8 providers versus 2, so concentration risk also matters.
Choose Mistral Large when vision-heavy evaluation, lower input-token cost, and broader provider choice are central to the workload. Choose Phi 3.5 Mini Instruct 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.
FAQ
Which has a larger context window, Mistral Large or Phi 3.5 Mini Instruct?
Phi 3.5 Mini Instruct supports 128k 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.
Which is cheaper, Mistral Large or Phi 3.5 Mini Instruct?
Mistral Large is cheaper on tracked token pricing. Mistral Large costs $0.32/1M input and $0.96/1M output tokens. Phi 3.5 Mini Instruct costs $0.90/1M input and $0.90/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Mistral Large or Phi 3.5 Mini Instruct open source?
Mistral Large is listed under Mistral License. Phi 3.5 Mini Instruct is listed under MIT. 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 Phi 3.5 Mini Instruct?
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 Phi 3.5 Mini Instruct?
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 Phi 3.5 Mini Instruct?
Mistral Large is available on NVIDIA NIM, Microsoft Foundry, AWS Bedrock, Mistral AI Studio, and IBM watsonx. Phi 3.5 Mini Instruct is available on Fireworks AI and NVIDIA NIM. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-05-22. Data sourced from public model cards and provider documentation.