Llama 4 Scout 17B-16E Instruct vs Qwen3.6-27B
Llama 4 Scout 17B-16E Instruct (2025) and Qwen3.6-27B (2026) compare a standalone API model against a coding-specialized model. Llama 4 Scout 17B-16E Instruct ships a 10m-token context window, while Qwen3.6-27B ships a 262k-token context window. On MMLU PRO, Qwen3.6-27B leads by 11.9 pts. On pricing, Llama 4 Scout 17B-16E Instruct costs $0.08/1M input tokens versus $0.32/1M for the alternative. This page treats the result as workflow and deployment fit, not a universal model winner.
Treat this as a product-type comparison: Llama 4 Scout 17B-16E Instruct is standalone API model, while Qwen3.6-27B is coding-specialized model. Choose based on workflow fit before reading any benchmark or price row as decisive.
Decision scorecard
Local evidence first| Signal | Llama 4 Scout 17B-16E Instruct | Qwen3.6-27B |
|---|---|---|
| Product type | Standalone API model | Coding-specialized model |
| Best for | multimodal apps, long-context analysis, and provider-routed production | custom coding agents, code generation, and tool loops |
| Decision fit | Coding, RAG, and Agents | Coding, RAG, and Agents |
| Context window | 10m | 262k |
| Cheapest output | $0.30/1M tokens | $3.20/1M tokens |
| Provider routes | 12 tracked | 4 tracked |
| Shared benchmarks | 2 rows | 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 the lower cheapest tracked output price at $0.30/1M tokens.
- Llama 4 Scout 17B-16E Instruct has broader tracked provider coverage for fallback and procurement flexibility.
- Llama 4 Scout 17B-16E Instruct uniquely exposes Structured outputs in local model data.
- Local decision data tags Llama 4 Scout 17B-16E Instruct for Coding, RAG, and Agents.
- Qwen3.6-27B holds a shared-benchmark lead on MMLU PRO, ahead by 11.9 points.
- Qwen3.6-27B uniquely exposes Reasoning, Function calling, and Tool use in local model data.
- Local decision data tags Qwen3.6-27B 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
Qwen3.6-27B
$1,056
Cheapest tracked route/tier: OpenRouter
Estimated monthly gap: $917. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on OpenRouter, Vercel AI Gateway, and Novita AI; start route-level A/B tests there.
- Qwen3.6-27B is $2.90/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Check replacement coverage for Structured outputs before moving production traffic.
- Qwen3.6-27B adds Reasoning, Function calling, and Tool use in local capability data.
- Provider overlap exists on OpenRouter, Vercel AI Gateway, and Novita AI; start route-level A/B tests there.
- Llama 4 Scout 17B-16E Instruct is $2.90/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Reasoning, Function calling, and Tool use before moving production traffic.
- Llama 4 Scout 17B-16E Instruct adds Structured outputs in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-04-05 | 2026-04-27 |
| Context window | 10m | 262k |
| Parameters | 109B (17B active) | 27B |
| Architecture | mixture of experts | dense |
| License | Llama 4 Community | Apache 2.0(OSI) |
| Openness | Open weights | Open source |
| Commercial use | Commercial use with conditions | Commercial use allowed |
| Knowledge cutoff | 2024-08 | - |
Pricing and availability
| Pricing attribute | Llama 4 Scout 17B-16E Instruct | Qwen3.6-27B |
|---|---|---|
| Input price | $0.08/1M tokens | $0.32/1M tokens |
| Output price | $0.30/1M tokens | $3.20/1M tokens |
| Providers |
Capabilities
| Capability | Llama 4 Scout 17B-16E Instruct | Qwen3.6-27B |
|---|---|---|
| Vision | Yes | Yes |
| Multimodal | Yes | Yes |
| Reasoning | No | Yes |
| Function calling | No | Yes |
| Tool use | No | Yes |
| Structured outputs | Yes | No |
| Code execution | No | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
| Benchmark | Llama 4 Scout 17B-16E Instruct | Qwen3.6-27B |
|---|---|---|
| MMLU PRO | 74.3 | 86.2 |
| LiveCodeBench | 32.8 | 83.9 |
Deep dive
On shared benchmark coverage, MMLU PRO has Llama 4 Scout 17B-16E Instruct at 74.3 and Qwen3.6-27B at 86.2, with Qwen3.6-27B ahead by 11.9 points; LiveCodeBench has Llama 4 Scout 17B-16E Instruct at 32.8 and Qwen3.6-27B at 83.9, with Qwen3.6-27B ahead by 51.1 points. The largest visible gap is 51.1 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 reasoning mode: Qwen3.6-27B, function calling: Qwen3.6-27B, tool use: Qwen3.6-27B, and structured outputs: Llama 4 Scout 17B-16E Instruct. 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.
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 Qwen3.6-27B lists $0.32/1M input and $3.20/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama 4 Scout 17B-16E Instruct lower by about $1.04 per million blended tokens. Availability is 12 providers versus 4, 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 Qwen3.6-27B when coding workflow support 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 Qwen3.6-27B?
Llama 4 Scout 17B-16E Instruct supports 10m tokens, while Qwen3.6-27B 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 Qwen3.6-27B?
Llama 4 Scout 17B-16E Instruct is cheaper on tracked token pricing. Llama 4 Scout 17B-16E Instruct costs $0.08/1M input and $0.30/1M output tokens. Qwen3.6-27B costs $0.32/1M input and $3.20/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Llama 4 Scout 17B-16E Instruct or Qwen3.6-27B open source?
Llama 4 Scout 17B-16E Instruct is listed under Llama 4 Community. Qwen3.6-27B 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 Qwen3.6-27B?
Both Llama 4 Scout 17B-16E Instruct and Qwen3.6-27B 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 Qwen3.6-27B?
Both Llama 4 Scout 17B-16E Instruct and Qwen3.6-27B 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 Qwen3.6-27B?
Llama 4 Scout 17B-16E Instruct is available on Cloudflare Workers AI, OpenRouter, Together AI, Fireworks AI, and DeepInfra. Qwen3.6-27B is available on OpenRouter, Alibaba Cloud PAI-EAS, Vercel AI Gateway, and Novita AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
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Last reviewed: 2026-06-07. Data sourced from public model cards and provider documentation.