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Llama 3.1 Swallow 8B Instruct vs Mistral Medium 3.5

Llama 3.1 Swallow 8B Instruct (2025) and Mistral Medium 3.5 (2026) are frontier reasoning models from Tokyo Institute of Technology and MistralAI. Llama 3.1 Swallow 8B Instruct ships a 4K-token context window, while Mistral Medium 3.5 ships a 256K-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 Medium 3.5 fits 64x more tokens; pick it for long-context work and Llama 3.1 Swallow 8B Instruct for tighter calls.

Decision scorecard

Local evidence first
SignalLlama 3.1 Swallow 8B InstructMistral Medium 3.5
Decision fitGeneralCoding, RAG, and Agents
Context window4K256K
Cheapest output-$7.5/1M tokens
Provider routes1 tracked2 tracked
Shared benchmarks0 rows0 rows

Decision tradeoffs

Choose Llama 3.1 Swallow 8B Instruct when...
  • Use Llama 3.1 Swallow 8B Instruct when your own prompt tests beat the comparison signals; the local data does not show a decisive standalone advantage yet.
Choose Mistral Medium 3.5 when...
  • Mistral Medium 3.5 has the larger context window for long prompts, retrieval packs, or transcript analysis.
  • Mistral Medium 3.5 has broader tracked provider coverage for fallback and procurement flexibility.
  • Mistral Medium 3.5 uniquely exposes Vision, Multimodal, and Reasoning in local model data.
  • Local decision data tags Mistral Medium 3.5 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 8B Instruct

Unavailable

No complete token price in local provider data

Mistral Medium 3.5

$3,075

Cheapest tracked route: Mistral AI Studio

Cost delta unavailable until both models have sourced input and output token prices.

Switch friction

Llama 3.1 Swallow 8B Instruct -> Mistral Medium 3.5
  • No overlapping tracked provider route is sourced for Llama 3.1 Swallow 8B Instruct and Mistral Medium 3.5; plan for SDK, billing, or endpoint changes.
  • Mistral Medium 3.5 adds Vision, Multimodal, and Reasoning in local capability data.
Mistral Medium 3.5 -> Llama 3.1 Swallow 8B Instruct
  • No overlapping tracked provider route is sourced for Mistral Medium 3.5 and Llama 3.1 Swallow 8B Instruct; plan for SDK, billing, or endpoint changes.
  • Check replacement coverage for Vision, Multimodal, and Reasoning before moving production traffic.

Specs

Specification
Released2025-01-012026-04-29
Context window4K256K
Parameters8B128B
Architecturedecoder onlydecoder only
License1Mistral License
Knowledge cutoff--

Pricing and availability

Pricing attributeLlama 3.1 Swallow 8B InstructMistral Medium 3.5
Input price-$1.5/1M tokens
Output price-$7.5/1M tokens
Providers

Capabilities

CapabilityLlama 3.1 Swallow 8B InstructMistral Medium 3.5
VisionNoYes
MultimodalNoYes
ReasoningNoYes
Function callingNoYes
Tool useNoYes
Structured outputsNoYes
Code executionNoNo

Benchmarks

No shared benchmark rows are currently sourced for this pair.

Deep dive

The capability footprint differs most on vision: Mistral Medium 3.5, multimodal input: Mistral Medium 3.5, reasoning mode: Mistral Medium 3.5, function calling: Mistral Medium 3.5, tool use: Mistral Medium 3.5, and structured outputs: Mistral Medium 3.5. 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 8B Instruct has no token price sourced yet and Mistral Medium 3.5 has $1.5/1M input tokens. Provider availability is 1 tracked routes versus 2. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.

Choose Llama 3.1 Swallow 8B Instruct when provider fit are central to the workload. Choose Mistral Medium 3.5 when reasoning depth, 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 8B Instruct or Mistral Medium 3.5?

Mistral Medium 3.5 supports 256K tokens, while Llama 3.1 Swallow 8B 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 8B Instruct or Mistral Medium 3.5 open source?

Llama 3.1 Swallow 8B Instruct is listed under 1. Mistral Medium 3.5 is listed under Mistral License. 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 8B Instruct or Mistral Medium 3.5?

Mistral Medium 3.5 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 8B Instruct or Mistral Medium 3.5?

Mistral Medium 3.5 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 reasoning mode, Llama 3.1 Swallow 8B Instruct or Mistral Medium 3.5?

Mistral Medium 3.5 has the clearer documented reasoning mode signal in this comparison. If reasoning mode 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 8B Instruct and Mistral Medium 3.5?

Llama 3.1 Swallow 8B Instruct is available on NVIDIA NIM. Mistral Medium 3.5 is available on Mistral AI Studio and OpenRouter. Provider coverage can affect latency, region availability, compliance posture, and fallback options.

Continue comparing

Last reviewed: 2026-05-14. Data sourced from public model cards and provider documentation.