Llama 3 70B Instruct vs Qwen3.5-397B-A17B
Llama 3 70B Instruct (2024) and Qwen3.5-397B-A17B (2026) are compact production models from AI at Meta and Alibaba. Llama 3 70B Instruct ships a 8K-token context window, while Qwen3.5-397B-A17B ships a 262K-token context window. On MMLU PRO, Qwen3.5-397B-A17B leads by 30.4 pts. 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.
Pick Qwen3.5-397B-A17B for general evaluation; Llama 3 70B Instruct is better when provider fit matters more.
Specs
| Released | 2024-04-18 | 2026-02-16 |
| Context window | 8K | 262K |
| Parameters | 70B | 397B |
| Architecture | decoder only | MoE |
| License | Open Source | Apache 2.0 |
| Knowledge cutoff | - | - |
Pricing and availability
| Llama 3 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 70B Instruct | Qwen3.5-397B-A17B | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
| Benchmark | Llama 3 70B Instruct | Qwen3.5-397B-A17B |
|---|---|---|
| MMLU PRO | 57.4 | 87.8 |
| Instruction-Following Evaluation | 77.8 | 92.6 |
Deep dive
On shared benchmark coverage, MMLU PRO has Llama 3 70B Instruct at 57.4 and Qwen3.5-397B-A17B at 87.8, with Qwen3.5-397B-A17B ahead by 30.4 points; Instruction-Following Evaluation has Llama 3 70B Instruct at 77.8 and Qwen3.5-397B-A17B at 92.6, with Qwen3.5-397B-A17B ahead by 14.8 points. The largest visible gap is 30.4 points on MMLU PRO, which matters most when that benchmark mirrors your workload. Treat isolated benchmark wins as directional, because provider routing, prompt style, and tool access can move real application results.
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 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 70B Instruct lower by about $0.57 per million blended tokens. Availability is 18 providers versus 1, so concentration risk also matters.
Choose Llama 3 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.
FAQ
Which has a larger context window, Llama 3 70B Instruct or Qwen3.5-397B-A17B?
Qwen3.5-397B-A17B supports 262K tokens, while Llama 3 70B Instruct supports 8K 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 70B Instruct or Qwen3.5-397B-A17B?
Qwen3.5-397B-A17B is cheaper on tracked token pricing. Llama 3 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 70B Instruct or Qwen3.5-397B-A17B open source?
Llama 3 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 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 70B Instruct or Qwen3.5-397B-A17B?
Both Llama 3 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 70B Instruct and Qwen3.5-397B-A17B?
Llama 3 70B Instruct is available on GCP Vertex AI, AWS Bedrock, Microsoft Foundry, NVIDIA NIM, and DeepInfra. Qwen3.5-397B-A17B is available on OpenRouter. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
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Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.