Qwen2-VL-72B-Instruct vs Qwen3.5-397B-A17B
Qwen2-VL-72B-Instruct (2025) and Qwen3.5-397B-A17B (2026) are frontier reasoning models from Alibaba. Qwen2-VL-72B-Instruct ships a 32K-token context window, while Qwen3.5-397B-A17B ships a 262K-token context window. On Massive Multi-discipline Multimodal Understanding, Qwen3.5-397B-A17B leads by 20.5 pts. On pricing, Qwen3.5-397B-A17B costs $0.39/1M input tokens versus $0.9/1M 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 ~131% cheaper at $0.39/1M; pay for Qwen2-VL-72B-Instruct only for vision-heavy evaluation.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-01-01 | 2026-02-16 |
| Context window | 32K | 262K |
| Parameters | 72B | 397B |
| Architecture | decoder only | MoE |
| License | Apache 2.0 | Apache 2.0 |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Qwen2-VL-72B-Instruct | Qwen3.5-397B-A17B |
|---|---|---|
| Input price | $0.9/1M tokens | $0.39/1M tokens |
| Output price | $0.9/1M tokens | $2.34/1M tokens |
| Providers |
Capabilities
| Capability | Qwen2-VL-72B-Instruct | Qwen3.5-397B-A17B |
|---|---|---|
| Vision | Yes | No |
| Multimodal | Yes | Yes |
| Reasoning | No | Yes |
| Function calling | No | Yes |
| Tool use | No | Yes |
| Structured outputs | No | Yes |
| Code execution | No | No |
Benchmarks
| Benchmark | Qwen2-VL-72B-Instruct | Qwen3.5-397B-A17B |
|---|---|---|
| Massive Multi-discipline Multimodal Understanding | 64.5 | 85.0 |
Deep dive
On shared benchmark coverage, Massive Multi-discipline Multimodal Understanding has Qwen2-VL-72B-Instruct at 64.5 and Qwen3.5-397B-A17B at 85, with Qwen3.5-397B-A17B ahead by 20.5 points. The largest visible gap is 20.5 points on Massive Multi-discipline Multimodal Understanding, 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 vision: Qwen2-VL-72B-Instruct, reasoning mode: Qwen3.5-397B-A17B, function calling: Qwen3.5-397B-A17B, tool use: Qwen3.5-397B-A17B, and structured outputs: Qwen3.5-397B-A17B. Both models share multimodal input, 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, Qwen2-VL-72B-Instruct lists $0.9/1M input and $0.9/1M 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 Qwen2-VL-72B-Instruct lower by about $0.07 per million blended tokens. Availability is 1 providers versus 1, so concentration risk also matters.
Choose Qwen2-VL-72B-Instruct when vision-heavy evaluation are central to the workload. Choose Qwen3.5-397B-A17B when reasoning depth, 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, Qwen2-VL-72B-Instruct or Qwen3.5-397B-A17B?
Qwen3.5-397B-A17B supports 262K tokens, while Qwen2-VL-72B-Instruct supports 32K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is cheaper, Qwen2-VL-72B-Instruct or Qwen3.5-397B-A17B?
Qwen3.5-397B-A17B is cheaper on tracked token pricing. Qwen2-VL-72B-Instruct costs $0.9/1M input and $0.9/1M 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 Qwen2-VL-72B-Instruct or Qwen3.5-397B-A17B open source?
Qwen2-VL-72B-Instruct is listed under Apache 2.0. 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 vision, Qwen2-VL-72B-Instruct or Qwen3.5-397B-A17B?
Qwen2-VL-72B-Instruct 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. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is better for multimodal input, Qwen2-VL-72B-Instruct or Qwen3.5-397B-A17B?
Both Qwen2-VL-72B-Instruct and Qwen3.5-397B-A17B expose multimodal input. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Where can I run Qwen2-VL-72B-Instruct and Qwen3.5-397B-A17B?
Qwen2-VL-72B-Instruct is available on Fireworks AI. Qwen3.5-397B-A17B is available on OpenRouter. Provider coverage can affect latency, region availability, compliance posture, and fallback options. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
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Last reviewed: 2026-05-11. Data sourced from public model cards and provider documentation.