Llama 3.2 1B Instruct vs Qwen3.5-397B-A17B
Llama 3.2 1B Instruct (2024) and Qwen3.5-397B-A17B (2026) are compact production models from AI at Meta and Alibaba. Llama 3.2 1B Instruct ships a 128K-token context window, while Qwen3.5-397B-A17B ships a 262K-token context window. On MMLU PRO, Qwen3.5-397B-A17B leads by 67.8 pts. On pricing, Llama 3.2 1B Instruct costs $0.03/1M input tokens versus $0.39/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Llama 3.2 1B Instruct is ~1344% cheaper at $0.03/1M; pay for Qwen3.5-397B-A17B only for long-context analysis.
Specs
| Released | 2024-09-25 | 2026-02-16 |
| Context window | 128K | 262K |
| Parameters | 1.23B | 397B |
| Architecture | decoder only | MoE |
| License | Open Source | Apache 2.0 |
| Knowledge cutoff | 2023-12 | - |
Pricing and availability
| Llama 3.2 1B Instruct | Qwen3.5-397B-A17B | |
|---|---|---|
| Input price | $0.03/1M tokens | $0.39/1M tokens |
| Output price | $0.2/1M tokens | $2.34/1M tokens |
| Providers |
Capabilities
| Llama 3.2 1B Instruct | Qwen3.5-397B-A17B | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
| Benchmark | Llama 3.2 1B Instruct | Qwen3.5-397B-A17B |
|---|---|---|
| MMLU PRO | 20.0 | 87.8 |
| Google-Proof Q&A | 25.6 | 89.3 |
| BFCL | 10.8 | 72.9 |
Deep dive
On shared benchmark coverage, MMLU PRO has Llama 3.2 1B Instruct at 20 and Qwen3.5-397B-A17B at 87.8, with Qwen3.5-397B-A17B ahead by 67.8 points; Google-Proof Q&A has Llama 3.2 1B Instruct at 25.6 and Qwen3.5-397B-A17B at 89.3, with Qwen3.5-397B-A17B ahead by 63.7 points; BFCL has Llama 3.2 1B Instruct at 10.8 and Qwen3.5-397B-A17B at 72.9, with Qwen3.5-397B-A17B ahead by 62.1 points. The largest visible gap is 67.8 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 3.2 1B Instruct lists $0.03/1M input and $0.2/1M 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 3.2 1B Instruct lower by about $0.9 per million blended tokens. Availability is 5 providers versus 1, so concentration risk also matters.
Choose Llama 3.2 1B Instruct when provider fit, lower input-token cost, and broader provider choice are central to the workload. Choose Qwen3.5-397B-A17B 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.
FAQ
Which has a larger context window, Llama 3.2 1B Instruct or Qwen3.5-397B-A17B?
Qwen3.5-397B-A17B supports 262K tokens, while Llama 3.2 1B 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 3.2 1B Instruct or Qwen3.5-397B-A17B?
Llama 3.2 1B Instruct is cheaper on tracked token pricing. Llama 3.2 1B Instruct costs $0.03/1M input and $0.2/1M 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 3.2 1B Instruct or Qwen3.5-397B-A17B open source?
Llama 3.2 1B 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 3.2 1B 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 3.2 1B Instruct or Qwen3.5-397B-A17B?
Both Llama 3.2 1B 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 3.2 1B Instruct and Qwen3.5-397B-A17B?
Llama 3.2 1B Instruct is available on OpenRouter, Fireworks AI, NVIDIA NIM, Bitdeer AI, and AWS Bedrock. 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.