Llama 3.1 Swallow 70B Instruct vs Mistral Large 2
Llama 3.1 Swallow 70B Instruct (2025) and Mistral Large 2 (2025) are compact production models from Tokyo Institute of Technology and MistralAI. Llama 3.1 Swallow 70B Instruct ships a 4K-token context window, while Mistral Large 2 ships a 128K-token context window. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing.
Mistral Large 2 fits 32x more tokens; pick it for long-context work and Llama 3.1 Swallow 70B Instruct for tighter calls.
Decision scorecard
Local evidence first| Signal | Llama 3.1 Swallow 70B Instruct | Mistral Large 2 |
|---|---|---|
| Decision fit | General | Coding, RAG, and Agents |
| Context window | 4K | 128K |
| Cheapest output | - | $2.4/1M tokens |
| Provider routes | 1 tracked | 4 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Use Llama 3.1 Swallow 70B Instruct when your own prompt tests beat the comparison signals; the local data does not show a decisive standalone advantage yet.
- Mistral Large 2 has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Mistral Large 2 has broader tracked provider coverage for fallback and procurement flexibility.
- Mistral Large 2 uniquely exposes Vision, Multimodal, and Function calling in local model data.
- Local decision data tags Mistral Large 2 for Coding, RAG, and Agents.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output prices on this page.
Llama 3.1 Swallow 70B Instruct
Unavailable
No complete token price in local provider data
Mistral Large 2
$984
Cheapest tracked route: AWS Bedrock
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- No overlapping tracked provider route is sourced for Llama 3.1 Swallow 70B Instruct and Mistral Large 2; plan for SDK, billing, or endpoint changes.
- Mistral Large 2 adds Vision, Multimodal, and Function calling in local capability data.
- No overlapping tracked provider route is sourced for Mistral Large 2 and Llama 3.1 Swallow 70B Instruct; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Vision, Multimodal, and Function calling before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-01-01 | 2025-11-25 |
| Context window | 4K | 128K |
| Parameters | 70B | 123B |
| Architecture | decoder only | decoder only |
| License | 1 | True |
| Knowledge cutoff | 2023 | 2025-07 |
Pricing and availability
| Pricing attribute | Llama 3.1 Swallow 70B Instruct | Mistral Large 2 |
|---|---|---|
| Input price | - | $0.48/1M tokens |
| Output price | - | $2.4/1M tokens |
| Providers |
Capabilities
| Capability | Llama 3.1 Swallow 70B Instruct | Mistral Large 2 |
|---|---|---|
| Vision | No | Yes |
| Multimodal | No | Yes |
| Reasoning | No | No |
| Function calling | No | Yes |
| Tool use | No | Yes |
| Structured outputs | No | Yes |
| Code execution | No | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on vision: Mistral Large 2, multimodal input: Mistral Large 2, function calling: Mistral Large 2, tool use: Mistral Large 2, and structured outputs: Mistral Large 2. 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.
Pricing coverage is uneven: Llama 3.1 Swallow 70B Instruct has no token price sourced yet and Mistral Large 2 has $0.48/1M input tokens. Provider availability is 1 tracked routes versus 4. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose Llama 3.1 Swallow 70B Instruct when provider fit are central to the workload. Choose Mistral Large 2 when long-context analysis, larger context windows, 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.
FAQ
Which has a larger context window, Llama 3.1 Swallow 70B Instruct or Mistral Large 2?
Mistral Large 2 supports 128K tokens, while Llama 3.1 Swallow 70B Instruct supports 4K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Is Llama 3.1 Swallow 70B Instruct or Mistral Large 2 open source?
Llama 3.1 Swallow 70B Instruct is listed under 1. Mistral Large 2 is listed under True. 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, Llama 3.1 Swallow 70B Instruct or Mistral Large 2?
Mistral Large 2 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.
Which is better for multimodal input, Llama 3.1 Swallow 70B Instruct or Mistral Large 2?
Mistral Large 2 has the clearer documented multimodal input signal in this comparison. If multimodal input is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for function calling, Llama 3.1 Swallow 70B Instruct or Mistral Large 2?
Mistral Large 2 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 Llama 3.1 Swallow 70B Instruct and Mistral Large 2?
Llama 3.1 Swallow 70B Instruct is available on NVIDIA NIM. Mistral Large 2 is available on OpenRouter, IBM watsonx, AWS Bedrock, and Mistral AI Studio. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-05-11. Data sourced from public model cards and provider documentation.