Llama 4 Maverick 17B Instruct FP8 vs Qwen3.6-Max
Llama 4 Maverick 17B Instruct FP8 (2025) and Qwen3.6-Max (2026) are general-purpose language models from AI at Meta and Alibaba. Llama 4 Maverick 17B Instruct FP8 ships a 1m-token context window, while Qwen3.6-Max ships a 262k-token context window. On Google-Proof Q&A, Qwen3.6-Max leads by 24.7 pts. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
Qwen3.6-Max is safer overall; choose Llama 4 Maverick 17B Instruct FP8 when long-context analysis matters.
Decision scorecard
Local evidence first| Signal | Llama 4 Maverick 17B Instruct FP8 | Qwen3.6-Max |
|---|---|---|
| Best for | multimodal apps, long-context analysis, and provider-routed production | multimodal apps |
| Decision fit | Coding, RAG, and Agents | Long context and Vision |
| Context window | 1m | 262k |
| Cheapest output | $0.60/1M tokens | - |
| Provider routes | 11 tracked | 1 tracked |
| Shared benchmarks | 1 shared | Google-Proof Q&A leader |
Decision tradeoffs
- Llama 4 Maverick 17B Instruct FP8 has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Llama 4 Maverick 17B Instruct FP8 has broader tracked provider coverage for fallback and procurement flexibility.
- Llama 4 Maverick 17B Instruct FP8 uniquely exposes Structured outputs in local model data.
- Local decision data tags Llama 4 Maverick 17B Instruct FP8 for Coding, RAG, and Agents.
- Qwen3.6-Max holds a shared-benchmark lead on Google-Proof Q&A, ahead by 24.7 points.
- Local decision data tags Qwen3.6-Max for Long context and Vision.
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 FP8
$270
Cheapest tracked route/tier: OpenRouter
Qwen3.6-Max
Unavailable
No complete token price in local provider data
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- No overlapping tracked provider route is sourced for Llama 4 Maverick 17B Instruct FP8 and Qwen3.6-Max; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Structured outputs before moving production traffic.
- No overlapping tracked provider route is sourced for Qwen3.6-Max and Llama 4 Maverick 17B Instruct FP8; plan for SDK, billing, or endpoint changes.
- Llama 4 Maverick 17B Instruct FP8 adds Structured outputs in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-04-05 | 2026-04-13 |
| Context window | 1m | 262k |
| Parameters | 400B (17B active) | — |
| Architecture | Mixture of Experts | - |
| License | Llama 4 Community | Apache 2.0OSI-approved |
| Openness | Open weights | Open source |
| Weights | Unknown | Unknown |
| Code | Unknown | Unknown |
| Commercial use | Commercial use: conditional | Commercial use: permitted |
| Knowledge cutoff | 2024-08 | - |
Pricing and availability
| Pricing attribute | Llama 4 Maverick 17B Instruct FP8 | Qwen3.6-Max |
|---|---|---|
| Input price | $0.15/1M tokens | - |
| Output price | $0.60/1M tokens | - |
| Providers |
Capabilities
| Capability | Llama 4 Maverick 17B Instruct FP8 | Qwen3.6-Max |
|---|---|---|
| Vision | Yes | Yes |
| Multimodal | Yes | Yes |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | Yes | No |
| Code execution | No | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
| Benchmark | Llama 4 Maverick 17B Instruct FP8 | Qwen3.6-Max |
|---|---|---|
| Google-Proof Q&A | 67.1 | 91.8 |
Deep dive
On shared benchmark coverage, Google-Proof Q&A has Llama 4 Maverick 17B Instruct FP8 at 67.1 and Qwen3.6-Max at 91.8, with Qwen3.6-Max ahead by 24.7 points. The largest visible gap is 24.7 points on Google-Proof Q&A, 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 structured outputs: Llama 4 Maverick 17B Instruct FP8. Both models share vision and 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.
Pricing coverage is uneven: Llama 4 Maverick 17B Instruct FP8 has $0.15/1M input tokens and Qwen3.6-Max has no token price sourced yet. Provider availability is 11 tracked routes versus 1. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose Llama 4 Maverick 17B Instruct FP8 when long-context analysis, larger context windows, and broader provider choice are central to the workload. Choose Qwen3.6-Max 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 Maverick 17B Instruct FP8 or Qwen3.6-Max?
Llama 4 Maverick 17B Instruct FP8 supports 1m tokens, while Qwen3.6-Max supports 262k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Is Llama 4 Maverick 17B Instruct FP8 or Qwen3.6-Max open source?
Llama 4 Maverick 17B Instruct FP8 is listed under Llama 4 Community. Qwen3.6-Max 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 FP8 or Qwen3.6-Max?
Both Llama 4 Maverick 17B Instruct FP8 and Qwen3.6-Max 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 Maverick 17B Instruct FP8 or Qwen3.6-Max?
Both Llama 4 Maverick 17B Instruct FP8 and Qwen3.6-Max expose multimodal input. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface.
Which is better for structured outputs, Llama 4 Maverick 17B Instruct FP8 or Qwen3.6-Max?
Llama 4 Maverick 17B Instruct FP8 has the clearer documented structured outputs signal in this comparison. If structured outputs is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run Llama 4 Maverick 17B Instruct FP8 and Qwen3.6-Max?
Llama 4 Maverick 17B Instruct FP8 is available on Microsoft Foundry, Together AI, OpenRouter, Fireworks AI, and DeepInfra. Qwen3.6-Max is available on Alibaba Cloud PAI-EAS. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
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Last reviewed: 2026-07-09. Data sourced from public model cards and provider documentation.