GPT-5.3-Codex vs Kimi K2.6
GPT-5.3-Codex (2026) and Kimi K2.6 (2026) are agentic coding models from OpenAI and Moonshot AI. GPT-5.3-Codex ships a 400k-token context window, while Kimi K2.6 ships a 262k-token context window. On SWE-bench Verified, GPT-5.3-Codex leads by 4.8 pts. On pricing, Kimi K2.6 costs $0.73/1M input tokens versus $1.75/1M for the alternative. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
Kimi K2.6 is ~140% cheaper at $0.73/1M; pay for GPT-5.3-Codex only for coding workflow support.
Decision scorecard
Local evidence first| Signal | GPT-5.3-Codex | Kimi K2.6 |
|---|---|---|
| Best for | custom coding agents, code generation, and tool loops | custom coding agents, code generation, and tool loops |
| Decision fit | Coding, RAG, and Agents | Coding, RAG, and Agents |
| Context window | 400k | 262k |
| Cheapest output | $14/1M tokens | $3.49/1M tokens |
| Provider routes | 3 tracked | 8 tracked |
| Shared benchmarks | SWE-bench Verified leader | 3 rows |
Decision tradeoffs
- GPT-5.3-Codex holds a shared-benchmark lead on SWE-bench Verified, ahead by 4.8 points.
- GPT-5.3-Codex has the larger context window for long prompts, retrieval packs, or transcript analysis.
- GPT-5.3-Codex uniquely exposes Code execution and Computer use in local model data.
- Local decision data tags GPT-5.3-Codex for Coding, RAG, and Agents.
- Kimi K2.6 holds a shared-benchmark lead on SWE-bench Pro, ahead by 1.8 points.
- Kimi K2.6 has the lower cheapest tracked output price at $3.49/1M tokens.
- Kimi K2.6 has broader tracked provider coverage for fallback and procurement flexibility.
- Kimi K2.6 uniquely exposes Multimodal in local model data.
- Local decision data tags Kimi K2.6 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-5.3-Codex
$4,900
Cheapest tracked route/tier: OpenRouter
Kimi K2.6
$1,457
Cheapest tracked route/tier: OpenRouter
Estimated monthly gap: $3,444. 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.
- Kimi K2.6 is $10.51/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Code execution and Computer use before moving production traffic.
- Kimi K2.6 adds Multimodal in local capability data.
- Provider overlap exists on OpenRouter and Vercel AI Gateway; start route-level A/B tests there.
- GPT-5.3-Codex is $10.51/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Check replacement coverage for Multimodal before moving production traffic.
- GPT-5.3-Codex adds Code execution and Computer use in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2026-02-05 | 2026-04-20 |
| Context window | 400k | 262k |
| Parameters | — | 1T |
| Architecture | decoder only | Mixture of Experts (MoE) |
| License | Proprietary | MIT(OSI) |
| Openness | Proprietary | Open source |
| Commercial use | Commercial use with conditions | Commercial use allowed |
| Knowledge cutoff | 2025-08 | 2025-04 |
Pricing and availability
| Pricing attribute | GPT-5.3-Codex | Kimi K2.6 |
|---|---|---|
| Input price | $1.75/1M tokens | $0.73/1M tokens |
| Output price | $14/1M tokens | $3.49/1M tokens |
| Providers |
Capabilities
| Capability | GPT-5.3-Codex | Kimi K2.6 |
|---|---|---|
| Vision | Yes | Yes |
| Multimodal | No | Yes |
| Reasoning | Yes | Yes |
| Function calling | Yes | Yes |
| Tool use | Yes | Yes |
| Structured outputs | Yes | Yes |
| Code execution | Yes | No |
| IDE integration | No | No |
| Computer use | Yes | No |
| Parallel agents | No | No |
Benchmarks
| Benchmark | GPT-5.3-Codex | Kimi K2.6 |
|---|---|---|
| SWE-bench Verified | 85.0 | 80.2 |
| SWE-bench Pro | 56.8 | 58.6 |
| Terminal-Bench 2.0 | 77.3 | 66.7 |
Deep dive
On shared benchmark coverage, SWE-bench Verified has GPT-5.3-Codex at 85 and Kimi K2.6 at 80.2, with GPT-5.3-Codex ahead by 4.8 points; SWE-bench Pro has GPT-5.3-Codex at 56.8 and Kimi K2.6 at 58.6, with Kimi K2.6 ahead by 1.8 points; Terminal-Bench 2.0 has GPT-5.3-Codex at 77.3 and Kimi K2.6 at 66.7, with GPT-5.3-Codex ahead by 10.6 points. The largest visible gap is 10.6 points on Terminal-Bench 2.0, 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 multimodal input: Kimi K2.6 and code execution: GPT-5.3-Codex. Both models share vision, reasoning mode, function calling, and tool use, 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-5.3-Codex lists $1.75/1M input and $14/1M output tokens on the cheapest tracked provider, while Kimi K2.6 lists $0.73/1M input and $3.49/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Kimi K2.6 lower by about $3.87 per million blended tokens. Availability is 3 providers versus 8, so concentration risk also matters.
Choose GPT-5.3-Codex when coding workflow support and larger context windows are central to the workload. Choose Kimi K2.6 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-5.3-Codex or Kimi K2.6?
GPT-5.3-Codex supports 400k tokens, while Kimi K2.6 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-5.3-Codex or Kimi K2.6?
Kimi K2.6 is cheaper on tracked token pricing. GPT-5.3-Codex costs $1.75/1M input and $14/1M output tokens. Kimi K2.6 costs $0.73/1M input and $3.49/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is GPT-5.3-Codex or Kimi K2.6 open source?
GPT-5.3-Codex is listed under Proprietary. Kimi K2.6 is listed under MIT. 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-5.3-Codex or Kimi K2.6?
Both GPT-5.3-Codex and Kimi K2.6 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-5.3-Codex or Kimi K2.6?
Kimi K2.6 has the clearer documented multimodal input signal in this comparison. If multimodal input is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run GPT-5.3-Codex and Kimi K2.6?
GPT-5.3-Codex is available on OpenRouter, OpenAI API, and Vercel AI Gateway. Kimi K2.6 is available on Cloudflare Workers AI, NVIDIA NIM, Moonshot AI Kimi, Fireworks AI, and OpenRouter. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-06-10. Data sourced from public model cards and provider documentation.