Llama 4 Scout 17B-16E Instruct vs Mistral Small 4
Llama 4 Scout 17B-16E Instruct (2025) and Mistral Small 4 (2026) are general-purpose language models from AI at Meta and MistralAI. Llama 4 Scout 17B-16E Instruct ships a 10m-token context window, while Mistral Small 4 ships a 256k-token context window. On MMLU PRO, Mistral Small 4 leads by 3.7 pts. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
Llama 4 Scout 17B-16E Instruct fits 39x more tokens; pick it for long-context work and Mistral Small 4 for tighter calls.
Decision scorecard
Local evidence first| Signal | Llama 4 Scout 17B-16E Instruct | Mistral Small 4 |
|---|---|---|
| Best for | multimodal apps, long-context analysis, and provider-routed production | multimodal apps, tool-calling agents, and provider-routed production |
| Decision fit | Coding, RAG, and Agents | RAG, Agents, and Long context |
| Context window | 10m | 256k |
| Cheapest output | $0.30/1M tokens | $0.30/1M tokens |
| Provider routes | 12 tracked | 3 tracked |
| Shared benchmarks | 2 shared | MMLU PRO leader |
Decision tradeoffs
- Llama 4 Scout 17B-16E Instruct has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Llama 4 Scout 17B-16E Instruct has broader tracked provider coverage for fallback and route flexibility.
- Llama 4 Scout 17B-16E Instruct uniquely exposes Structured outputs in local model data.
- Local decision data tags Llama 4 Scout 17B-16E Instruct for Coding, RAG, and Agents.
- Mistral Small 4 holds a shared-benchmark lead on MMLU PRO, ahead by 3.7 points.
- Mistral Small 4 uniquely exposes JSON / Tool use in local model data.
- Local decision data tags Mistral Small 4 for RAG, Agents, and Long context.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
Llama 4 Scout 17B-16E Instruct
$139
Cheapest tracked route/tier: OpenRouter
Mistral Small 4
$155
Cheapest tracked route/tier: Mistral AI Studio
Estimated monthly gap: $16.00. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on OpenRouter and NVIDIA NIM; start route-level A/B tests there.
- Cheapest tracked output pricing is tied, so migration risk shifts to quality, latency, and provider packaging.
- Check replacement coverage for Structured outputs before moving production traffic.
- Mistral Small 4 adds JSON / Tool use in local capability data.
- Provider overlap exists on OpenRouter and NVIDIA NIM; start route-level A/B tests there.
- Cheapest tracked output pricing is tied, so migration risk shifts to quality, latency, and provider packaging.
- Check replacement coverage for JSON / Tool use before moving production traffic.
- Llama 4 Scout 17B-16E Instruct adds Structured outputs in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-04-05 | 2026-03-16 |
| Context window | 10m | 256k |
| Parameters | 109B (17B active) | 119B (6.5B active) |
| Architecture | Mixture of Experts | Mixture of Experts |
| License | Llama 4 Community | Apache 2.0OSI-approved |
| Openness | Open weights | Open source |
| Weights | Unknown | Available |
| Code | Unknown | Unknown |
| Commercial use | Commercial use: conditional | Commercial use: permitted |
| Knowledge cutoff | 2024-08 | 2025-06 |
Pricing and availability
| Pricing attribute | Llama 4 Scout 17B-16E Instruct | Mistral Small 4 |
|---|---|---|
| Input price | $0.08/1M tokens | $0.10/1M tokens |
| Output price | $0.30/1M tokens | $0.30/1M tokens |
| Providers |
Capabilities
| Capability | Llama 4 Scout 17B-16E Instruct | Mistral Small 4 |
|---|---|---|
| Vision | Yes | Yes |
| Multimodal | Yes | Yes |
| Reasoning | No | No |
| JSON / Tool use | No | Yes |
| Structured outputs | Yes | No |
| Code execution | No | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
| Benchmark | Llama 4 Scout 17B-16E Instruct | Mistral Small 4 |
|---|---|---|
| MMLU PRO | 74.3 | 78.0 |
| τ-bench | 62.3 | 65.8 |
Continue comparing
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Last reviewed: 2026-07-09. Data sourced from public model cards and provider documentation.