GPT-5-Codex vs Kimi K2 Thinking
Too close to call on our weighted score (GPT-5-Codex 42, Kimi K2 Thinking 42). The right pick depends on what you value most.
OpenAIGPT-5-Codex
42/100- ECI—
- Price$1.25 / $10.00
- Context400K
Moonshot AIKimi K2 Thinking
42/100- ECI146.0
- Price$0.60 / $2.50
- Context262K
Add a model
Make it a three-way comparison.
Too close to call
It is close. Our weighted score puts them within a point (GPT-5-Codex 42/100, Kimi K2 Thinking 42/100), so choose by what matters most for your work: Kimi K2 Thinking on price and GPT-5-Codex for long inputs. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
- CapabilityNot enough dataNo independent benchmark covers every model here yet
- Lowest priceKimi K2 ThinkingKimi K2 Thinking $1.07 · GPT-5-Codex $3.44 per 1M tokens (3:1 blend)
- Longest contextGPT-5-CodexGPT-5-Codex 400,000 · Kimi K2 Thinking 262,144 tokens
- Widest inputsGPT-5-CodexGPT-5-Codex: Text, Images · Kimi K2 Thinking: Text
- Self-hostingKimi K2 ThinkingPublishes downloadable weights
| Measure | Weight | GPT-5-Codex | Kimi K2 Thinking |
|---|---|---|---|
| Price | 50% | 24 | 48 |
| Inputs & features | 30% | 70 | 35 |
| Context window | 20% | 44 | 37 |
| Overall | 100% | 42/100 | 42/100 |
Left out because at least one model lacks the data: capability. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GPT-5-CodexOpenAI | Kimi K2 ThinkingMoonshot AI |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | — | 146.0 |
| ECI rank | — | #72 of 148 |
| GPQA DiamondGraduate-level science questions | — | 84.2% |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 83.1% |
| Price per million tokens | ||
| Input | $1.25 | $0.60 (best) |
| Output | $10.00 | $2.50 (best) |
| Cached input | — | — |
| Blended (3:1) | $3.44 | $1.07 (best) |
| Long-context rate | Same rate | Same rate |
| Price source | Median of 3 providers | Median of 10 providers |
| Limits | ||
| Context window | 400,000 tokens (best) | 262,144 tokens |
| Max output | 128,000 tokens | 262,144 tokens (best) |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | Yes | No |
| PDFs | No | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | Yes | Yes |
| Tool calling | Yes | Yes |
| Structured output | Yes | No |
| Availability | ||
| Weights | Proprietary | Open |
| API model ID | — | — |
| API providers | 3 | 10 (best) |
| Released | Sep 15, 2025 | Nov 6, 2025 |
| Knowledge cutoff | Sep 30, 2024 | Aug 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- GPT-5-Codex$32.50
- Kimi K2 Thinking$11.00
Which should you choose?
Which is better: GPT-5-Codex or Kimi K2 Thinking?
It is close. Our weighted score puts them within a point (GPT-5-Codex 42/100, Kimi K2 Thinking 42/100), so choose by what matters most for your work: Kimi K2 Thinking on price and GPT-5-Codex for long inputs. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, GPT-5-Codex or Kimi K2 Thinking?
Kimi K2 Thinking is cheaper at $0.60 input / $2.50 output per million tokens (median across 10 API providers). GPT-5-Codex costs $1.25 input / $10.00 output per million tokens (median across 3 API providers). At a typical mix of three input tokens to one output token, that is $1.07 per million tokens for Kimi K2 Thinking versus $3.44 for GPT-5-Codex (3.2× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers both models yet. GPT-5-Codex has not been scored yet and Kimi K2 Thinking has an ECI of 146.0.
Which is better for coding?
There are no published SWE-bench Verified results for GPT-5-Codex and Kimi K2 Thinking yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Both support tool calling for agent workflows.
Which has the bigger context window?
GPT-5-Codex has the largest context window at 400,000 tokens, against 262,144 for Kimi K2 Thinking. Maximum output per response: GPT-5-Codex up to 128,000, Kimi K2 Thinking up to 262,144 tokens.
Which can read images, PDFs, audio or video?
GPT-5-Codex accepts text and images; Kimi K2 Thinking accepts text. GPT-5-Codex handles the widest range of inputs.
Are any of these open source?
Kimi K2 Thinking publishes its weights and can be self-hosted; GPT-5-Codex is proprietary.
Which is newer?
Kimi K2 Thinking is the newest, released Nov 6, 2025. GPT-5-Codex came out Sep 15, 2025. Knowledge cutoff: GPT-5-Codex Sep 30, 2024, Kimi K2 Thinking Aug 2024.
How do you decide the winner?
Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Oct 4, 2026.
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