Llama 3.1 70B Instruct vs Qwen3.5-235B-A22B-Instruct
Llama 3.1 70B Instruct (2024) and Qwen3.5-235B-A22B-Instruct (2026) are compact production models from AI at Meta and Alibaba. Llama 3.1 70B Instruct ships a 128K-token context window, while Qwen3.5-235B-A22B-Instruct ships a 512k-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. The goal is to make the tradeoff clear before deeper testing.
Qwen3.5-235B-A22B-Instruct fits 4x more tokens; pick it for long-context work and Llama 3.1 70B Instruct for tighter calls.
Specs
| Released | 2024-07-23 | 2026-02-24 |
| Context window | 128K | 512k |
| Parameters | 70B | 235B |
| Architecture | decoder only | MoE |
| License | Open Source | Apache 2.0 |
| Knowledge cutoff | - | - |
Pricing and availability
| Llama 3.1 70B Instruct | Qwen3.5-235B-A22B-Instruct | |
|---|---|---|
| Input price | $0.4/1M tokens | - |
| Output price | $0.4/1M tokens | - |
| Providers | - |
Capabilities
| Llama 3.1 70B Instruct | Qwen3.5-235B-A22B-Instruct | |
|---|---|---|
| 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 structured outputs: Llama 3.1 70B Instruct. 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 70B Instruct has $0.4/1M input tokens and Qwen3.5-235B-A22B-Instruct has no token price sourced yet. Provider availability is 11 tracked routes versus 0. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose Llama 3.1 70B Instruct when provider fit and broader provider choice are central to the workload. Choose Qwen3.5-235B-A22B-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. 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 Qwen3.5-235B-A22B-Instruct?
Qwen3.5-235B-A22B-Instruct supports 512k 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.
Is Llama 3.1 70B Instruct or Qwen3.5-235B-A22B-Instruct open source?
Llama 3.1 70B Instruct is listed under Open Source. Qwen3.5-235B-A22B-Instruct 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 structured outputs, Llama 3.1 70B Instruct or Qwen3.5-235B-A22B-Instruct?
Llama 3.1 70B Instruct has the clearer documented structured outputs signal in this comparison. If structured outputs 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 70B Instruct and Qwen3.5-235B-A22B-Instruct?
Llama 3.1 70B Instruct is available on OctoAI API, Together AI, Fireworks AI, NVIDIA NIM, and Microsoft Foundry. Qwen3.5-235B-A22B-Instruct is available on the tracked providers still being sourced. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Llama 3.1 70B Instruct over Qwen3.5-235B-A22B-Instruct?
Qwen3.5-235B-A22B-Instruct fits 4x more tokens; pick it for long-context work and Llama 3.1 70B Instruct for tighter calls. If your workload also depends on provider fit, start with Llama 3.1 70B Instruct; if it depends on long-context analysis, run the same evaluation with Qwen3.5-235B-A22B-Instruct.
Continue comparing
Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.