Llama 3.1 70B Instruct vs Qwen3.5-397B-A17B
Llama 3.1 70B Instruct (2024) and Qwen3.5-397B-A17B (2026) are compact production models from AI at Meta and Alibaba. Llama 3.1 70B Instruct ships a 128K-token context window, while Qwen3.5-397B-A17B ships a 262K-token context window. On pricing, Qwen3.5-397B-A17B costs $0.39/1M input tokens versus $0.4/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing.
Qwen3.5-397B-A17B is safer overall; choose Llama 3.1 70B Instruct when provider fit matters.
Specs
| Released | 2024-07-23 | 2026-02-16 |
| Context window | 128K | 262K |
| Parameters | 70B | 397B |
| Architecture | decoder only | MoE |
| License | Open Source | Apache 2.0 |
| Knowledge cutoff | - | - |
Pricing and availability
| Llama 3.1 70B Instruct | Qwen3.5-397B-A17B | |
|---|---|---|
| Input price | $0.4/1M tokens | $0.39/1M tokens |
| Output price | $0.4/1M tokens | $2.34/1M tokens |
| Providers |
Capabilities
| Llama 3.1 70B Instruct | Qwen3.5-397B-A17B | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on multimodal input: Qwen3.5-397B-A17B. Both models share structured outputs, 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, Llama 3.1 70B Instruct lists $0.4/1M input and $0.4/1M output tokens, while Qwen3.5-397B-A17B lists $0.39/1M input and $2.34/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama 3.1 70B Instruct lower by about $0.57 per million blended tokens. Availability is 11 providers versus 1, so concentration risk also matters.
Choose Llama 3.1 70B Instruct when provider fit and broader provider choice are central to the workload. Choose Qwen3.5-397B-A17B when long-context analysis, larger context windows, and lower input-token cost 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. For teams standardizing a stack, that distinction is often the difference between a benchmark win and a reliable deployment.
FAQ
Which has a larger context window, Llama 3.1 70B Instruct or Qwen3.5-397B-A17B?
Qwen3.5-397B-A17B supports 262K tokens, while Llama 3.1 70B Instruct supports 128K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Which is cheaper, Llama 3.1 70B Instruct or Qwen3.5-397B-A17B?
Qwen3.5-397B-A17B is cheaper on tracked token pricing. Llama 3.1 70B Instruct costs $0.4/1M input and $0.4/1M output tokens. Qwen3.5-397B-A17B costs $0.39/1M input and $2.34/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Llama 3.1 70B Instruct or Qwen3.5-397B-A17B open source?
Llama 3.1 70B Instruct is listed under Open Source. Qwen3.5-397B-A17B 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 multimodal input, Llama 3.1 70B Instruct or Qwen3.5-397B-A17B?
Qwen3.5-397B-A17B 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 structured outputs, Llama 3.1 70B Instruct or Qwen3.5-397B-A17B?
Both Llama 3.1 70B Instruct and Qwen3.5-397B-A17B expose structured outputs. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface.
Where can I run Llama 3.1 70B Instruct and Qwen3.5-397B-A17B?
Llama 3.1 70B Instruct is available on OctoAI API, Together AI, Fireworks AI, NVIDIA NIM, and Microsoft Foundry. Qwen3.5-397B-A17B is available on OpenRouter. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.