GLM-5.1 vs Kimi K2.7 Code vs o3-pro
Kimi K2.7 Code comes out ahead, 64 to 57 and 51 on our weighted score, and it is the cheaper option too.
Z.ai (Zhipu)GLM-5.1
57/100- ECI149.9
- Price$1.40 / $4.40
- Context200K
- Our pick
Moonshot AIKimi K2.7 Code
64/100- ECI150.0
- Price$0.95 / $4.00
- Context262K
OpenAIo3-pro
51/100- ECI147.4
- Price$20.00 / $80.00
- Context200K
Kimi K2.7 Code is our pick
Kimi K2.7 Code is the better all-round choice, scoring 64/100 against GLM-5.1 (57) and o3-pro (51). It leads on price, inputs & features and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityKimi K2.7 CodeCapabilities Index (ECI): Kimi K2.7 Code 150.0 · GLM-5.1 149.9 · o3-pro 147.4
- Lowest priceKimi K2.7 CodeKimi K2.7 Code $1.71 · GLM-5.1 $2.15 · o3-pro $35.00 per 1M tokens (3:1 blend)
- Longest contextKimi K2.7 CodeKimi K2.7 Code 262,144 · GLM-5.1 200,000 · o3-pro 200,000 tokens
- Widest inputsKimi K2.7 CodeGLM-5.1: Text · Kimi K2.7 Code: Text, Images, Video · o3-pro: Text, Images
- Self-hostingGLM-5.1 and Kimi K2.7 CodePublishes downloadable weights
| Measure | Weight | GLM-5.1 | Kimi K2.7 Code | o3-pro |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 78 | 78 | 75 |
| Price | 25% | 34 | 39 | 0 |
| Inputs & features | 15% | 45 | 80 | 70 |
| Context window | 10% | 32 | 37 | 32 |
| Overall | 100% | 57/100 | 64/100 | 51/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GLM-5.1Z.ai (Zhipu) | Kimi K2.7 CodeMoonshot AI | o3-proOpenAI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 149.9 | 150.0 (best) | 147.4 |
| ECI rank | #51 of 148 | #49 of 148 (best) | #60 of 148 |
| GPQA DiamondGraduate-level science questions | 89.9% (best) | 87.9% | — |
| FrontierMath Tiers 1–3Research-level mathematics | 36.8% | 54.0% (best) | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 93.3% | 95.6% (best) | — |
| SWE-bench VerifiedFixing real GitHub issues | 74.2% | — | — |
| SimpleQA VerifiedShort factual questions | 34.0% | 36.5% (best) | — |
| Price per million tokens | |||
| Input | $1.40 | $0.95 (best) | $20.00 |
| Output | $4.40 | $4.00 (best) | $80.00 |
| Cached input | $0.26 | $0.19 (best) | — |
| Blended (3:1) | $2.15 | $1.71 (best) | $35.00 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Official Moonshot AI API | Official OpenAI API |
| Limits | |||
| Context window | 200,000 tokens | 262,144 tokens (best) | 200,000 tokens |
| Max output | 131,072 tokens | 262,144 tokens (best) | 100,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | Yes | No |
| Reasoning | Yes | Yes | Yeslow · medium · high |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | Yes | Yes |
| Availability | |||
| Weights | Open | Open | Proprietary |
| API model ID | glm-5.1 | kimi-k2.7-code | o3-pro |
| API providers | 40 | 51 (best) | 6 |
| Released | Apr 7, 2026 | Jun 12, 2026 | Jun 10, 2025 |
| Knowledge cutoff | — | Jan 2025 | May 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- GLM-5.1$22.80
- Kimi K2.7 Code$17.50
- o3-pro$360.00
Which should you choose?
Which is better: GLM-5.1, Kimi K2.7 Code or o3-pro?
Kimi K2.7 Code is the better all-round choice, scoring 64/100 against GLM-5.1 (57) and o3-pro (51). It leads on price, inputs & features and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, GLM-5.1, Kimi K2.7 Code or o3-pro?
Kimi K2.7 Code is cheaper at $0.95 input / $4.00 output per million tokens (official Moonshot AI API price). GLM-5.1 costs $1.40 input / $4.40 output per million tokens (official Z.AI API price); o3-pro costs $20.00 input / $80.00 output per million tokens (official OpenAI API price). At a typical mix of three input tokens to one output token, that is $1.71 per million tokens for Kimi K2.7 Code versus $2.15 for GLM-5.1 (1.3× as much) and $35.00 for o3-pro (20× as much).
Which scores higher on benchmarks?
Kimi K2.7 Code scores higher on the Capabilities Index (ECI): Kimi K2.7 Code 150.0 (#49 of 148), GLM-5.1 149.9 (#51 of 148) and o3-pro 147.4 (#60 of 148). The confidence ranges of the top two overlap (148.1–151.8 vs 148.0–151.6), so treat the gap as small.
Which is better for coding?
There are no published SWE-bench Verified results for Kimi K2.7 Code and o3-pro yet, so there is no like-for-like coding score. On overall capability, Kimi K2.7 Code 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 K2.7 Code has the largest context window at 262,144 tokens, against 200,000 for GLM-5.1 and 200,000 for o3-pro. Maximum output per response: GLM-5.1 up to 131,072, Kimi K2.7 Code up to 262,144, o3-pro up to 100,000 tokens.
Which can read images, PDFs, audio or video?
GLM-5.1 accepts text; Kimi K2.7 Code accepts text, images and video; o3-pro accepts text and images. Kimi K2.7 Code handles the widest range of inputs.
Are any of these open source?
GLM-5.1 and Kimi K2.7 Code publishes its weights and can be self-hosted; o3-pro is proprietary.
Which is newer?
Kimi K2.7 Code is the newest, released Jun 12, 2026. GLM-5.1 came out Apr 7, 2026; o3-pro came out Jun 10, 2025. Knowledge cutoff: Kimi K2.7 Code Jan 2025, o3-pro May 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.
