Llama 4 Scout 17B-16E Instruct vs Qwen3.5-9B
Llama 4 Scout 17B-16E Instruct (2025) and Qwen3.5-9B (2026) are general-purpose language models from AI at Meta and Alibaba. Llama 4 Scout 17B-16E Instruct ships a 10m-token context window, while Qwen3.5-9B ships a 262k-token context window. On MMLU PRO, Qwen3.5-9B leads by 8.2 pts. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
Llama 4 Scout 17B-16E Instruct fits 38x more tokens; pick it for long-context work and Qwen3.5-9B for tighter calls.
Decision scorecard
Local evidence first| Signal | Llama 4 Scout 17B-16E Instruct | Qwen3.5-9B |
|---|---|---|
| Best for | multimodal apps, long-context analysis, and provider-routed production | multimodal apps, tool-calling agents, and provider-routed production |
| Decision fit | Coding, RAG, and Agents | Coding, RAG, and Agents |
| Context window | 10m | 262k |
| Cheapest output | $0.30/1M tokens | $0.15/1M tokens |
| Provider routes | 12 tracked | 3 tracked |
| Shared benchmarks | 3 shared | MMLU PRO leader |
Decision tradeoffs
- Llama 4 Scout 17B-16E Instruct has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Llama 4 Scout 17B-16E Instruct has broader tracked provider coverage for fallback and procurement flexibility.
- Local decision data tags Llama 4 Scout 17B-16E Instruct for Coding, RAG, and Agents.
- Qwen3.5-9B holds a shared-benchmark lead on MMLU PRO, ahead by 8.2 points.
- Qwen3.5-9B has the lower cheapest tracked output price at $0.15/1M tokens.
- Qwen3.5-9B uniquely exposes Function calling and Tool use in local model data.
- Local decision data tags Qwen3.5-9B 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 4 Scout 17B-16E Instruct
$139
Cheapest tracked route/tier: OpenRouter
Qwen3.5-9B
$118
Cheapest tracked route/tier: Together AI
Estimated monthly gap: $21.50. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on Together AI and OpenRouter; start route-level A/B tests there.
- Qwen3.5-9B is $0.15/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Qwen3.5-9B adds Function calling and Tool use in local capability data.
- Provider overlap exists on OpenRouter and Together AI; start route-level A/B tests there.
- Llama 4 Scout 17B-16E Instruct is $0.15/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Check replacement coverage for Function calling and Tool use before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-04-05 | 2026-03-02 |
| Context window | 10m | 262k |
| Parameters | 109B (17B active) | 9B |
| Architecture | Mixture of Experts | Decoder Only |
| License | Llama 4 Community | Apache 2.0OSI-approved |
| Openness | Open weights | Open source |
| Commercial use | Commercial use: conditional | Commercial use: permitted |
| Knowledge cutoff | 2024-08 | - |
Pricing and availability
| Pricing attribute | Llama 4 Scout 17B-16E Instruct | Qwen3.5-9B |
|---|---|---|
| Input price | $0.08/1M tokens | $0.10/1M tokens |
| Output price | $0.30/1M tokens | $0.15/1M tokens |
| Providers |
Capabilities
| Capability | Llama 4 Scout 17B-16E Instruct | Qwen3.5-9B |
|---|---|---|
| Vision | Yes | Yes |
| Multimodal | Yes | Yes |
| Reasoning | No | No |
| 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 4 Scout 17B-16E Instruct | Qwen3.5-9B |
|---|---|---|
| MMLU PRO | 74.3 | 82.5 |
| LiveCodeBench | 32.8 | 65.6 |
| τ-bench | 62.3 | 79.1 |
Deep dive
On shared benchmark coverage, MMLU PRO has Llama 4 Scout 17B-16E Instruct at 74.3 and Qwen3.5-9B at 82.5, with Qwen3.5-9B ahead by 8.2 points; LiveCodeBench has Llama 4 Scout 17B-16E Instruct at 32.8 and Qwen3.5-9B at 65.6, with Qwen3.5-9B ahead by 32.8 points; τ-bench has Llama 4 Scout 17B-16E Instruct at 62.3 and Qwen3.5-9B at 79.1, with Qwen3.5-9B ahead by 16.8 points. The largest visible gap is 32.8 points on LiveCodeBench, 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 function calling: Qwen3.5-9B and tool use: Qwen3.5-9B. Both models share vision, multimodal input, 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 4 Scout 17B-16E Instruct lists $0.08/1M input and $0.30/1M output tokens on the cheapest tracked provider, while Qwen3.5-9B lists $0.10/1M input and $0.15/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Qwen3.5-9B lower by about $0.03 per million blended tokens. Availability is 12 providers versus 3, so concentration risk also matters.
Choose Llama 4 Scout 17B-16E Instruct when long-context analysis, larger context windows, and lower input-token cost are central to the workload. Choose Qwen3.5-9B when vision-heavy evaluation 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 4 Scout 17B-16E Instruct or Qwen3.5-9B?
Llama 4 Scout 17B-16E Instruct supports 10m tokens, while Qwen3.5-9B supports 262k 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 4 Scout 17B-16E Instruct or Qwen3.5-9B?
Qwen3.5-9B is cheaper on tracked token pricing. Llama 4 Scout 17B-16E Instruct costs $0.08/1M input and $0.30/1M output tokens. Qwen3.5-9B costs $0.10/1M input and $0.15/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Llama 4 Scout 17B-16E Instruct or Qwen3.5-9B open source?
Llama 4 Scout 17B-16E Instruct is listed under Llama 4 Community. Qwen3.5-9B 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 4 Scout 17B-16E Instruct or Qwen3.5-9B?
Both Llama 4 Scout 17B-16E Instruct and Qwen3.5-9B expose vision. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface.
Which is better for multimodal input, Llama 4 Scout 17B-16E Instruct or Qwen3.5-9B?
Both Llama 4 Scout 17B-16E Instruct and Qwen3.5-9B expose multimodal input. 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 4 Scout 17B-16E Instruct and Qwen3.5-9B?
Llama 4 Scout 17B-16E Instruct is available on Cloudflare Workers AI, OpenRouter, Together AI, Fireworks AI, and DeepInfra. Qwen3.5-9B is available on Together AI, OpenRouter, and Alibaba Cloud PAI-EAS. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
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Last reviewed: 2026-06-15. Data sourced from public model cards and provider documentation.