GPT-5.5 vs Qwen3.6-27B
GPT-5.5 (2026) and Qwen3.6-27B (2026) compare a standalone API model against a coding-specialized model. GPT-5.5 ships a 1.05m-token context window, while Qwen3.6-27B ships a 262k-token context window. On MMLU PRO, GPT-5.5 leads by 1.9 pts. On pricing, GPT-5.5 ranges from $5 to $10/1M input tokens by tier; Qwen3.6-27B costs $0.32/1M input tokens. This page treats the result as workflow and deployment fit, not a universal model winner.
Treat this as a product-type comparison: GPT-5.5 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 | GPT-5.5 | Qwen3.6-27B |
|---|---|---|
| Product type | Standalone API model | Coding-specialized model |
| Best for | reasoning-heavy apps, multimodal apps, and tool-calling agents | custom coding agents, code generation, and tool loops |
| Decision fit | Coding, RAG, and Agents | Coding, RAG, and Agents |
| Context window | 1.05m | 262k |
| Cheapest output | $30/1M tokens | $3.20/1M tokens |
| Provider routes | 3 tracked | 4 tracked |
| Shared benchmarks | MMLU PRO leader | 6 rows |
Decision tradeoffs
- GPT-5.5 holds a shared-benchmark lead on MMLU PRO, ahead by 1.9 points.
- GPT-5.5 has the larger context window for long prompts, retrieval packs, or transcript analysis.
- GPT-5.5 uniquely exposes Structured outputs and Code execution in local model data.
- Local decision data tags GPT-5.5 for Coding, RAG, and Agents.
- Qwen3.6-27B has the lower cheapest tracked output price at $3.20/1M tokens.
- Qwen3.6-27B has broader tracked provider coverage for fallback and procurement flexibility.
- 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.
GPT-5.5
$11,500
Cheapest tracked route/tier: OpenAI API 0-272K input tokens
Qwen3.6-27B
$1,056
Cheapest tracked route/tier: OpenRouter
Estimated monthly gap: $10,444. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on OpenRouter and Vercel AI Gateway; start route-level A/B tests there.
- Qwen3.6-27B is $26.80/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Structured outputs and Code execution before moving production traffic.
- Provider overlap exists on OpenRouter and Vercel AI Gateway; start route-level A/B tests there.
- GPT-5.5 is $26.80/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- GPT-5.5 adds Structured outputs and Code execution in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2026-04-23 | 2026-04-27 |
| Context window | 1.05m | 262k |
| Parameters | — | 27B |
| Architecture | decoder only | dense |
| License | Proprietary | Apache 2.0(OSI) |
| Openness | Proprietary | Open source |
| Commercial use | Commercial use with conditions | Commercial use allowed |
| Knowledge cutoff | 2025-12 | - |
Pricing and availability
| Pricing attribute | GPT-5.5 | Qwen3.6-27B |
|---|---|---|
| Input price |
| $0.32/1M tokens |
| Output price |
| $3.20/1M tokens |
| Providers |
Capabilities
| Capability | GPT-5.5 | Qwen3.6-27B |
|---|---|---|
| Vision | Yes | Yes |
| Multimodal | Yes | Yes |
| Reasoning | Yes | Yes |
| Function calling | Yes | Yes |
| Tool use | Yes | Yes |
| Structured outputs | Yes | No |
| Code execution | Yes | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
| Benchmark | GPT-5.5 | Qwen3.6-27B |
|---|---|---|
| MMLU PRO | 88.1 | 86.2 |
| SWE-bench Verified | 82.6 | 77.2 |
| SWE-bench Pro | 58.6 | 53.5 |
| Google-Proof Q&A | 93.6 | 87.8 |
| Humanity's Last Exam | 41.4 | 24.0 |
| MMMU Pro | 88.3 | 75.8 |
Deep dive
On shared benchmark coverage, MMLU PRO has GPT-5.5 at 88.1 and Qwen3.6-27B at 86.2, with GPT-5.5 ahead by 1.9 points; SWE-bench Verified has GPT-5.5 at 82.6 and Qwen3.6-27B at 77.2, with GPT-5.5 ahead by 5.4 points; SWE-bench Pro has GPT-5.5 at 58.6 and Qwen3.6-27B at 53.5, with GPT-5.5 ahead by 5.1 points. The largest visible gap is 5.4 points on SWE-bench Verified, 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: GPT-5.5 and code execution: GPT-5.5. Both models share vision, multimodal input, reasoning mode, and function calling, 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, GPT-5.5 lists tiered pricing: 0-272K input tokens is $5/1M input and $30/1M output; 0-272,000t is $5/1M input and $30/1M output; 272K+ input tokens is $8/1M input and $36/1M output; 272,000t+ is $10/1M input and $45/1M output, 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 Qwen3.6-27B lower by about $11.32 per million blended tokens. For tiered rows, this cheapest-track view can understate interactive or fast-lane spend, so compare the tier you will actually use. Availability is 3 providers versus 4, so concentration risk also matters.
Choose GPT-5.5 when coding workflow support and larger context windows are central to the workload. Choose Qwen3.6-27B when coding workflow support, 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, GPT-5.5 or Qwen3.6-27B?
GPT-5.5 supports 1.05m 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. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is cheaper, GPT-5.5 or Qwen3.6-27B?
GPT-5.5 lists tiered pricing: 0-272K input tokens is $5/1M input and $30/1M output; 0-272,000t is $5/1M input and $30/1M output; 272K+ input tokens is $8/1M input and $36/1M output; 272,000t+ is $10/1M input and $45/1M output. Qwen3.6-27B lists $0.32/1M input and $3.20/1M output tokens on the cheapest tracked provider. Compare the tier you will actually use; cheap async pricing can overstate savings for interactive workflows. Provider discounts or batch pricing can still change the final bill.
Is GPT-5.5 or Qwen3.6-27B open source?
GPT-5.5 is listed under Proprietary. 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, GPT-5.5 or Qwen3.6-27B?
Both GPT-5.5 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. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is better for multimodal input, GPT-5.5 or Qwen3.6-27B?
Both GPT-5.5 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. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Where can I run GPT-5.5 and Qwen3.6-27B?
GPT-5.5 is available on OpenAI API, OpenRouter, and Vercel AI Gateway. 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.
Continue comparing
Last reviewed: 2026-06-08. Data sourced from public model cards and provider documentation.