Llama 4 Maverick 17B Instruct vs Qwen3.5-9B
Llama 4 Maverick 17B Instruct (2025) and Qwen3.5-9B (2026) are general-purpose language models from AI at Meta and Alibaba. Llama 4 Maverick 17B Instruct ships a 1m-token context window, while Qwen3.5-9B ships a 262k-token context window. On MMLU PRO, Qwen3.5-9B leads by 2 pts. On pricing, Qwen3.5-9B costs $0.10/1M input tokens versus $0.24/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Qwen3.5-9B is ~140% cheaper at $0.10/1M; pay for Llama 4 Maverick 17B Instruct only for long-context analysis.
Decision scorecard
Local evidence first| Signal | Llama 4 Maverick 17B 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 Long context | RAG, Agents, and Long context |
| Context window | 1m | 262k |
| Cheapest output | $0.97/1M tokens | $0.15/1M tokens |
| Provider routes | 2 tracked | 3 tracked |
| Shared benchmarks | 1 rows | MMLU PRO leader |
Decision tradeoffs
- Llama 4 Maverick 17B Instruct has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Local decision data tags Llama 4 Maverick 17B Instruct for Coding, RAG, and Long context.
- Qwen3.5-9B leads the largest shared benchmark signal on MMLU PRO by 2 points.
- Qwen3.5-9B has the lower cheapest tracked output price at $0.15/1M tokens.
- Qwen3.5-9B has broader tracked provider coverage for fallback and procurement flexibility.
- Qwen3.5-9B uniquely exposes Vision, Function calling, and Tool use in local model data.
- Local decision data tags Qwen3.5-9B for RAG, Agents, and Long context.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
Llama 4 Maverick 17B Instruct
$435
Cheapest tracked route/tier: AWS Bedrock
Qwen3.5-9B
$118
Cheapest tracked route/tier: Together AI
Estimated monthly gap: $317. 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 4 Maverick 17B Instruct and Qwen3.5-9B; plan for SDK, billing, or endpoint changes.
- Qwen3.5-9B is $0.82/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Qwen3.5-9B adds Vision, Function calling, and Tool use in local capability data.
- No overlapping tracked provider route is sourced for Qwen3.5-9B and Llama 4 Maverick 17B Instruct; plan for SDK, billing, or endpoint changes.
- Llama 4 Maverick 17B Instruct is $0.82/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Check replacement coverage for Vision, Function calling, and Tool use before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-04-05 | 2026-03-02 |
| Context window | 1m | 262k |
| Parameters | 400B (17B active) | 9B |
| Architecture | - | decoder only |
| License | Proprietary | Apache 2.0 |
| Knowledge cutoff | 2024-08 | - |
Pricing and availability
| Pricing attribute | Llama 4 Maverick 17B Instruct | Qwen3.5-9B |
|---|---|---|
| Input price | $0.24/1M tokens | $0.10/1M tokens |
| Output price | $0.97/1M tokens | $0.15/1M tokens |
| Providers |
Capabilities
| Capability | Llama 4 Maverick 17B Instruct | Qwen3.5-9B |
|---|---|---|
| Vision | No | 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 Maverick 17B Instruct | Qwen3.5-9B |
|---|---|---|
| MMLU PRO | 80.5 | 82.5 |
Deep dive
On shared benchmark coverage, MMLU PRO has Llama 4 Maverick 17B Instruct at 80.5 and Qwen3.5-9B at 82.5, with Qwen3.5-9B ahead by 2 points. The largest visible gap is 2 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 vision: Qwen3.5-9B, function calling: Qwen3.5-9B, and tool use: Qwen3.5-9B. Both models share 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 Maverick 17B Instruct lists $0.24/1M input and $0.97/1M 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 $0.34 per million blended tokens. Availability is 2 providers versus 3, so concentration risk also matters.
Choose Llama 4 Maverick 17B Instruct when long-context analysis and larger context windows are central to the workload. Choose Qwen3.5-9B when vision-heavy evaluation, lower input-token cost, 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 4 Maverick 17B Instruct or Qwen3.5-9B?
Llama 4 Maverick 17B Instruct supports 1m 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 Maverick 17B Instruct or Qwen3.5-9B?
Qwen3.5-9B is cheaper on tracked token pricing. Llama 4 Maverick 17B Instruct costs $0.24/1M input and $0.97/1M 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 Maverick 17B Instruct or Qwen3.5-9B open source?
Llama 4 Maverick 17B Instruct is listed under Proprietary. 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 Maverick 17B Instruct or Qwen3.5-9B?
Qwen3.5-9B 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, Llama 4 Maverick 17B Instruct or Qwen3.5-9B?
Both Llama 4 Maverick 17B 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 Maverick 17B Instruct and Qwen3.5-9B?
Llama 4 Maverick 17B Instruct is available on AWS Bedrock and Vercel AI Gateway. 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.
Continue comparing
Last reviewed: 2026-05-22. Data sourced from public model cards and provider documentation.