Llama 4 Maverick 17B Instruct FP8 vs Qwen2-7B-Instruct
Llama 4 Maverick 17B Instruct FP8 (2025) and Qwen2-7B-Instruct (2024) are compact production models from AI at Meta and Alibaba. Llama 4 Maverick 17B Instruct FP8 ships a 1M-token context window, while Qwen2-7B-Instruct ships a 128K-token context window. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing.
Llama 4 Maverick 17B Instruct FP8 fits 8x more tokens; pick it for long-context work and Qwen2-7B-Instruct for tighter calls.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-04-05 | 2024-06-07 |
| Context window | 1M | 128K |
| Parameters | 17B | 7B |
| Architecture | mixture of experts | decoder only |
| License | Open Source | 1 |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Llama 4 Maverick 17B Instruct FP8 | Qwen2-7B-Instruct |
|---|---|---|
| Input price | $0.15/1M tokens | - |
| Output price | $0.6/1M tokens | - |
| Providers |
Capabilities
| Capability | Llama 4 Maverick 17B Instruct FP8 | Qwen2-7B-Instruct |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | Yes | No |
| Code execution | No | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on structured outputs: Llama 4 Maverick 17B Instruct FP8. Both models share the core language-model surface, 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 Qwen2-7B-Instruct has no token price sourced yet. Provider availability is 7 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 Qwen2-7B-Instruct when provider fit are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship. This keeps the decision grounded in measurable tradeoffs instead of brand-level assumptions. It also helps separate model capability from provider packaging, which can change cost and latency. For teams standardizing a stack, that distinction is often the difference between a benchmark win and a reliable deployment.
FAQ
Which has a larger context window, Llama 4 Maverick 17B Instruct FP8 or Qwen2-7B-Instruct?
Llama 4 Maverick 17B Instruct FP8 supports 1M tokens, while Qwen2-7B-Instruct supports 128K 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 Qwen2-7B-Instruct open source?
Llama 4 Maverick 17B Instruct FP8 is listed under Open Source. Qwen2-7B-Instruct is listed under 1. 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 structured outputs, Llama 4 Maverick 17B Instruct FP8 or Qwen2-7B-Instruct?
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 Qwen2-7B-Instruct?
Llama 4 Maverick 17B Instruct FP8 is available on Microsoft Foundry, Together AI, OpenRouter, Fireworks AI, and DeepInfra. Qwen2-7B-Instruct is available on NVIDIA NIM. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Llama 4 Maverick 17B Instruct FP8 over Qwen2-7B-Instruct?
Llama 4 Maverick 17B Instruct FP8 fits 8x more tokens; pick it for long-context work and Qwen2-7B-Instruct for tighter calls. If your workload also depends on long-context analysis, start with Llama 4 Maverick 17B Instruct FP8; if it depends on provider fit, run the same evaluation with Qwen2-7B-Instruct.
Continue comparing
Last reviewed: 2026-05-11. Data sourced from public model cards and provider documentation.