Llama 3.2 11B Vision vs Qwen3.5-397B-A17B
Llama 3.2 11B Vision (2024) and Qwen3.5-397B-A17B (2026) are frontier reasoning models from AI at Meta and Alibaba. Llama 3.2 11B Vision 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 41.4 pts. On pricing, Llama 3.2 11B Vision costs $0.20/1M input tokens versus $0.39/1M for the alternative. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
Llama 3.2 11B Vision is ~95% cheaper at $0.20/1M; pay for Qwen3.5-397B-A17B only for reasoning depth.
Decision scorecard
Local evidence first| Signal | Llama 3.2 11B Vision | Qwen3.5-397B-A17B |
|---|---|---|
| Best for | multimodal apps | reasoning-heavy apps, multimodal apps, and tool-calling agents |
| Decision fit | RAG, Long context, and Vision | Coding, RAG, and Agents |
| Context window | 128k | 262k |
| Cheapest output | $0.27/1M tokens | $2.34/1M tokens |
| Provider routes | 1 tracked | 4 tracked |
| Shared benchmarks | 2 shared | MMLU PRO leader |
Decision tradeoffs
- Llama 3.2 11B Vision has the lower cheapest tracked output price at $0.27/1M tokens.
- Local decision data tags Llama 3.2 11B Vision for RAG, Long context, and Vision.
- Qwen3.5-397B-A17B holds a shared-benchmark lead on MMLU PRO, ahead by 41.4 points.
- Qwen3.5-397B-A17B has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Qwen3.5-397B-A17B has broader tracked provider coverage for fallback and procurement flexibility.
- Qwen3.5-397B-A17B uniquely exposes Multimodal, Reasoning, and Function calling in local model data.
- Local decision data tags Qwen3.5-397B-A17B for Coding, RAG, and Agents.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
Llama 3.2 11B Vision
$228
Cheapest tracked route/tier: AWS Bedrock
Qwen3.5-397B-A17B
$897
Cheapest tracked route/tier: OpenRouter
Estimated monthly gap: $670. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- No overlapping tracked provider route is sourced for Llama 3.2 11B Vision and Qwen3.5-397B-A17B; plan for SDK, billing, or endpoint changes.
- Qwen3.5-397B-A17B is $2.07/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Qwen3.5-397B-A17B adds Multimodal, Reasoning, and Function calling in local capability data.
- No overlapping tracked provider route is sourced for Qwen3.5-397B-A17B and Llama 3.2 11B Vision; plan for SDK, billing, or endpoint changes.
- Llama 3.2 11B Vision is $2.07/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Multimodal, Reasoning, and Function calling before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-09-25 | 2026-02-16 |
| Context window | 128k | 262k |
| Parameters | 10.6B | 397B |
| Architecture | Decoder Only | Mixture of Experts |
| License | Llama 3 Community | Apache 2.0OSI-approved |
| Openness | Open weights | Open source |
| Weights | Unknown | Available |
| Code | Unknown | Unknown |
| Commercial use | Commercial use: conditional | Commercial use: permitted |
| Knowledge cutoff | 2024-03 | - |
Pricing and availability
| Pricing attribute | Llama 3.2 11B Vision | Qwen3.5-397B-A17B |
|---|---|---|
| Input price | $0.20/1M tokens | $0.39/1M tokens |
| Output price | $0.27/1M tokens | $2.34/1M tokens |
| Providers |
Capabilities
| Capability | Llama 3.2 11B Vision | Qwen3.5-397B-A17B |
|---|---|---|
| Vision | Yes | Yes |
| Multimodal | No | Yes |
| Reasoning | No | Yes |
| Function calling | No | Yes |
| Tool use | No | Yes |
| Structured outputs | Yes | Yes |
| Code execution | No | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
| Benchmark | Llama 3.2 11B Vision | Qwen3.5-397B-A17B |
|---|---|---|
| MMLU PRO | 46.4 | 87.8 |
| Massive Multi-discipline Multimodal Understanding | 50.7 | 85.0 |
Deep dive
On shared benchmark coverage, MMLU PRO has Llama 3.2 11B Vision at 46.4 and Qwen3.5-397B-A17B at 87.8, with Qwen3.5-397B-A17B ahead by 41.4 points; Massive Multi-discipline Multimodal Understanding has Llama 3.2 11B Vision at 50.7 and Qwen3.5-397B-A17B at 85, with Qwen3.5-397B-A17B ahead by 34.3 points. The largest visible gap is 41.4 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, reasoning mode: Qwen3.5-397B-A17B, function calling: Qwen3.5-397B-A17B, and tool use: Qwen3.5-397B-A17B. Both models share vision and 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 11B Vision lists $0.20/1M input and $0.27/1M output tokens on the cheapest tracked provider, 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 11B Vision lower by about $0.75 per million blended tokens. Availability is 1 providers versus 4, so concentration risk also matters.
Choose Llama 3.2 11B Vision when vision-heavy evaluation and lower input-token cost are central to the workload. Choose Qwen3.5-397B-A17B when reasoning depth, larger context windows, and broader provider choice 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 11B Vision or Qwen3.5-397B-A17B?
Qwen3.5-397B-A17B supports 262k tokens, while Llama 3.2 11B Vision 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 11B Vision or Qwen3.5-397B-A17B?
Llama 3.2 11B Vision is cheaper on tracked token pricing. Llama 3.2 11B Vision costs $0.20/1M input and $0.27/1M 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 11B Vision or Qwen3.5-397B-A17B open source?
Llama 3.2 11B Vision is listed under Llama 3 Community. 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, Llama 3.2 11B Vision or Qwen3.5-397B-A17B?
Both Llama 3.2 11B Vision and Qwen3.5-397B-A17B expose vision. 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.
Which is better for multimodal input, Llama 3.2 11B Vision 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.
Where can I run Llama 3.2 11B Vision and Qwen3.5-397B-A17B?
Llama 3.2 11B Vision is available on AWS Bedrock. Qwen3.5-397B-A17B is available on OpenRouter, Together AI, Alibaba Cloud PAI-EAS, and Novita AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
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Last reviewed: 2026-06-29. Data sourced from public model cards and provider documentation.