GPT-5.5 vs Kimi K2.5
GPT-5.5 (2026) and Kimi K2.5 (2026) compare a standalone API model against a coding-specialized model. GPT-5.5 ships a 1.05m-token context window, while Kimi K2.5 ships a 256k-token context window. On MMLU PRO, GPT-5.5 leads by 1 pts. On pricing, GPT-5.5 ranges from $5 to $8/1M input tokens by tier; Kimi K2.5 costs $0.44/1M 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 Kimi K2.5 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 | Kimi K2.5 |
|---|---|---|
| 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 | 256k |
| Cheapest output | $30/1M tokens | $2/1M tokens |
| Provider routes | 3 tracked | 10 tracked |
| Shared benchmarks | MMLU PRO leader | 9 rows |
Decision tradeoffs
- GPT-5.5 holds a shared-benchmark lead on MMLU PRO, ahead by 1 points.
- GPT-5.5 has the larger context window for long prompts, retrieval packs, or transcript analysis.
- GPT-5.5 uniquely exposes Reasoning, Tool use, and Code execution in local model data.
- Local decision data tags GPT-5.5 for Coding, RAG, and Agents.
- Kimi K2.5 holds a shared-benchmark lead on AIME 2025, ahead by 14.9 points.
- Kimi K2.5 has the lower cheapest tracked output price at $2/1M tokens.
- Kimi K2.5 has broader tracked provider coverage for fallback and procurement flexibility.
- Local decision data tags Kimi K2.5 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
Kimi K2.5
$852
Cheapest tracked route/tier: OpenRouter
Estimated monthly gap: $10,648. 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.
- Kimi K2.5 is $28/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Reasoning, Tool use, 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 $28/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- GPT-5.5 adds Reasoning, Tool use, and Code execution in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2026-04-23 | 2026-03-15 |
| Context window | 1.05m | 256k |
| Parameters | — | 1T (MoE, 384 experts) |
| Architecture | decoder only | mixture of experts |
| License | Proprietary | Proprietary |
| Openness | Proprietary | Proprietary |
| Commercial use | Commercial use with conditions | Commercial use with conditions |
| Knowledge cutoff | 2025-12 | - |
Pricing and availability
| Pricing attribute | GPT-5.5 | Kimi K2.5 |
|---|---|---|
| Input price |
| $0.44/1M tokens |
| Output price |
| $2/1M tokens |
| Providers |
Capabilities
| Capability | GPT-5.5 | Kimi K2.5 |
|---|---|---|
| Vision | Yes | Yes |
| Multimodal | Yes | Yes |
| Reasoning | Yes | No |
| Function calling | Yes | Yes |
| Tool use | Yes | No |
| Structured outputs | Yes | Yes |
| Code execution | Yes | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
| Benchmark | GPT-5.5 | Kimi K2.5 |
|---|---|---|
| MMLU PRO | 88.1 | 87.1 |
| SWE-bench Verified | 82.6 | 76.8 |
| Google-Proof Q&A | 93.6 | 87.9 |
| AIME 2025 | 81.2 | 96.1 |
| Humanity's Last Exam | 41.4 | 50.2 |
| MCP-Atlas | 75.3 | 29.5 |
| Terminal-Bench 2.0 | 82.7 | 50.8 |
| BrowseComp | 84.4 | 60.6 |
| MMMU Pro | 88.3 | 78.5 |
Deep dive
On shared benchmark coverage, MMLU PRO has GPT-5.5 at 88.1 and Kimi K2.5 at 87.1, with GPT-5.5 ahead by 1 points; SWE-bench Verified has GPT-5.5 at 82.6 and Kimi K2.5 at 76.8, with GPT-5.5 ahead by 5.8 points; Google-Proof Q&A has GPT-5.5 at 93.6 and Kimi K2.5 at 87.9, with GPT-5.5 ahead by 5.7 points. The largest visible gap is 5.8 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 reasoning mode: GPT-5.5, tool use: GPT-5.5, and code execution: GPT-5.5. Both models share vision, multimodal input, function calling, 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, GPT-5.5 lists tiered pricing: 0-272K input tokens is $5/1M input and $30/1M output; 272K+ input tokens is $8/1M input and $36/1M output, while Kimi K2.5 lists $0.44/1M input and $2/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Kimi K2.5 lower by about $11.59 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 10, so concentration risk also matters.
Choose GPT-5.5 when coding workflow support and larger context windows are central to the workload. Choose Kimi K2.5 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 Kimi K2.5?
GPT-5.5 supports 1.05m tokens, while Kimi K2.5 supports 256k 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 Kimi K2.5?
GPT-5.5 lists tiered pricing: 0-272K input tokens is $5/1M input and $30/1M output; 272K+ input tokens is $8/1M input and $36/1M output. Kimi K2.5 lists $0.44/1M input and $2/1M 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 Kimi K2.5 open source?
GPT-5.5 is listed under Proprietary. Kimi K2.5 is listed under Proprietary. 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 Kimi K2.5?
Both GPT-5.5 and Kimi K2.5 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 Kimi K2.5?
Both GPT-5.5 and Kimi K2.5 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 Kimi K2.5?
GPT-5.5 is available on OpenAI API, OpenRouter, and Vercel AI Gateway. Kimi K2.5 is available on Cloudflare Workers AI, Fireworks AI, OpenRouter, Together AI, and NVIDIA NIM. 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.