GPT-5.2 Pro vs Kimi K3 vs Claude Opus 4.6
Kimi K3 comes out ahead, 65 to 61 and 56 on our weighted score, and it is the cheaper option too.
OpenAIGPT-5.2 Pro
56/100- ECI155.4
- Price$21.00 / $168.00
- Context400K
- Our pick
Moonshot AIKimi K3
65/100- ECI157.6
- Price$3.00 / $15.00
- Context1.05M
AnthropicClaude Opus 4.6
61/100- ECI155.3
- Price$5.00 / $25.00
- Context1M
Kimi K3 is our pick
Kimi K3 is the better all-round choice, scoring 65/100 against Claude Opus 4.6 (61) and GPT-5.2 Pro (56). It leads on capability and price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityKimi K3Capabilities Index (ECI): Kimi K3 157.6 · GPT-5.2 Pro 155.4 · Claude Opus 4.6 155.3
- Lowest priceKimi K3Kimi K3 $6.00 · Claude Opus 4.6 $10.00 · GPT-5.2 Pro $57.75 per 1M tokens (3:1 blend)
- Longest contextKimi K3Kimi K3 1,048,576 · Claude Opus 4.6 1,000,000 · GPT-5.2 Pro 400,000 tokens
- Widest inputsKimi K3 and Claude Opus 4.6GPT-5.2 Pro: Text, Images · Kimi K3: Text, Images, Video · Claude Opus 4.6: Text, Images, PDFs
- Self-hostingKimi K3Publishes downloadable weights
| Measure | Weight | GPT-5.2 Pro | Kimi K3 | Claude Opus 4.6 |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 85 | 88 | 85 |
| Price | 25% | 0 | 13 | 2 |
| Inputs & features | 15% | 60 | 80 | 80 |
| Context window | 10% | 44 | 61 | 60 |
| Overall | 100% | 56/100 | 65/100 | 61/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GPT-5.2 ProOpenAI | Kimi K3Moonshot AI | Claude Opus 4.6Anthropic |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 155.4 | 157.6 (best) | 155.3 |
| ECI rank | #25 of 148 | #13 of 148 (best) | #27 of 148 |
| GPQA DiamondGraduate-level science questions | — | 93.1% (best) | 90.5% |
| FrontierMath Tiers 1–3Research-level mathematics | 74.0% (best) | 72.2% | 66.0% |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 97.2% (best) | 94.4% |
| SWE-bench VerifiedFixing real GitHub issues | — | — | 78.7% |
| SimpleQA VerifiedShort factual questions | — | 50.6% (best) | 47.0% |
| Price per million tokens | |||
| Input | $21.00 | $3.00 (best) | $5.00 |
| Output | $168.00 | $15.00 (best) | $25.00 |
| Cached input | — | $0.30 (best) | $0.50 |
| Blended (3:1) | $57.75 | $6.00 (best) | $10.00 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official OpenAI API | Official Moonshot AI API | Official Anthropic API |
| Limits | |||
| Context window | 400,000 tokens | 1,048,576 tokens (best) | 1,000,000 tokens |
| Max output | 128,000 tokens | 131,072 tokens (best) | 128,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | Yes |
| PDFs | No | No | Yes |
| Audio | No | No | No |
| Video | No | Yes | No |
| Reasoning | Yesmedium · high · xhigh | Yeslow · high · max | Yeslow · medium · high · max |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | Yes |
| Availability | |||
| Weights | Proprietary | Open | Proprietary |
| API model ID | gpt-5.2-pro | kimi-k3 | claude-opus-4-6 |
| API providers | 8 | 68 (best) | 33 |
| Released | Dec 11, 2025 | Jul 16, 2026 | Feb 5, 2026 |
| Knowledge cutoff | Aug 31, 2025 | — | May 31, 2025 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- GPT-5.2 Pro$546.00
- Kimi K3$60.00
- Claude Opus 4.6$100.00
Which should you choose?
Which is better: GPT-5.2 Pro, Kimi K3 or Claude Opus 4.6?
Kimi K3 is the better all-round choice, scoring 65/100 against Claude Opus 4.6 (61) and GPT-5.2 Pro (56). It leads on capability and price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, GPT-5.2 Pro, Kimi K3 or Claude Opus 4.6?
Kimi K3 is cheaper at $3.00 input / $15.00 output per million tokens (official Moonshot AI API price). Claude Opus 4.6 costs $5.00 input / $25.00 output per million tokens (official Anthropic API price); GPT-5.2 Pro costs $21.00 input / $168.00 output per million tokens (official OpenAI API price). At a typical mix of three input tokens to one output token, that is $6.00 per million tokens for Kimi K3 versus $10.00 for Claude Opus 4.6 (1.7× as much) and $57.75 for GPT-5.2 Pro (9.6× as much).
Which scores higher on benchmarks?
Kimi K3 scores higher on the Capabilities Index (ECI): Kimi K3 157.6 (#13 of 148), GPT-5.2 Pro 155.4 (#25 of 148) and Claude Opus 4.6 155.3 (#27 of 148). The confidence ranges of the top two overlap (154.9–160.4 vs 153.0–158.2), so treat the gap as small. On individual benchmarks: FrontierMath Tiers 1–3 — GPT-5.2 Pro 74.0%, Kimi K3 72.2%, Claude Opus 4.6 66.0%.
Which is better for coding?
There are no published SWE-bench Verified results for GPT-5.2 Pro and Kimi K3 yet, so there is no like-for-like coding score. On overall capability, Kimi K3 leads, which tends to carry over to coding, but test on your own codebase. All three support tool calling for agent workflows.
Which has the bigger context window?
Kimi K3 has the largest context window at 1,048,576 tokens, against 1,000,000 for Claude Opus 4.6 and 400,000 for GPT-5.2 Pro. Maximum output per response: GPT-5.2 Pro up to 128,000, Kimi K3 up to 131,072, Claude Opus 4.6 up to 128,000 tokens.
Which can read images, PDFs, audio or video?
GPT-5.2 Pro accepts text and images; Kimi K3 accepts text, images and video; Claude Opus 4.6 accepts text, images and PDFs. Kimi K3 handles the widest range of inputs.
Are any of these open source?
Kimi K3 publishes its weights and can be self-hosted; GPT-5.2 Pro and Claude Opus 4.6 is proprietary.
Which is newer?
Kimi K3 is the newest, released Jul 16, 2026. Claude Opus 4.6 came out Feb 5, 2026; GPT-5.2 Pro came out Dec 11, 2025. Knowledge cutoff: GPT-5.2 Pro Aug 31, 2025, Claude Opus 4.6 May 31, 2025.
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.
