Kimi K2.7 Code vs GLM-5 vs Grok 4.3
Too close to call on our weighted score (Grok 4.3 67, Kimi K2.7 Code 64, GLM-5 55). The right pick depends on what you value most.
Moonshot AIKimi K2.7 Code
64/100- ECI150.0
- Price$0.95 / $4.00
- Context262K
Z.ai (Zhipu)GLM-5
55/100- ECI145.8
- Price$1.00 / $3.20
- Context205K
xAIGrok 4.3
67/100- ECI149.2
- Price$1.25 / $2.50
- Context1M
Too close to call
It is close. Our weighted score puts them within 2 points (Grok 4.3 67/100, Kimi K2.7 Code 64/100, GLM-5 55/100), so choose by what matters most for your work: Kimi K2.7 Code for raw capability, GLM-5 on price and Grok 4.3 for long inputs. 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 · Grok 4.3 149.2 · GLM-5 145.8
- Lowest priceGLM-5GLM-5 $1.55 · Grok 4.3 $1.56 · Kimi K2.7 Code $1.71 per 1M tokens (3:1 blend)
- Longest contextGrok 4.3Grok 4.3 1,000,000 · Kimi K2.7 Code 262,144 · GLM-5 204,800 tokens
- Widest inputsKimi K2.7 Code and Grok 4.3Kimi K2.7 Code: Text, Images, Video · GLM-5: Text · Grok 4.3: Text, Images, PDFs
- Self-hostingKimi K2.7 Code and GLM-5Publishes downloadable weights
| Measure | Weight | Kimi K2.7 Code | GLM-5 | Grok 4.3 |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 78 | 73 | 77 |
| Price | 25% | 39 | 41 | 41 |
| Inputs & features | 15% | 80 | 35 | 80 |
| Context window | 10% | 37 | 32 | 60 |
| Overall | 100% | 64/100 | 55/100 | 67/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Kimi K2.7 CodeMoonshot AI | GLM-5Z.ai (Zhipu) | Grok 4.3xAI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 150.0 (best) | 145.8 | 149.2 |
| ECI rank | #49 of 148 (best) | #74 of 148 | #55 of 148 |
| GPQA DiamondGraduate-level science questions | 87.9% | 87.8% | 88.8% (best) |
| FrontierMath Tiers 1–3Research-level mathematics | 54.0% (best) | — | 42.8% |
| OTIS Mock AIME 2024–2025Competition mathematics | 95.6% (best) | 80.0% | 93.3% |
| SWE-bench VerifiedFixing real GitHub issues | — | 72.1% | — |
| SimpleQA VerifiedShort factual questions | 36.5% (best) | — | 33.2% |
| Price per million tokens | |||
| Input | $0.95 (best) | $1.00 | $1.25 |
| Output | $4.00 | $3.20 | $2.50 (best) |
| Cached input | $0.19 (best) | $0.20 | $0.20 |
| Blended (3:1) | $1.71 | $1.55 (best) | $1.56 |
| Long-context rate | Same rate | Same rate | Over 200K: $2.50 / $5.00 |
| Price source | Official Moonshot AI API | Official Z.AI API | Official xAI API |
| Limits | |||
| Context window | 262,144 tokens | 204,800 tokens | 1,000,000 tokens (best) |
| Max output | 262,144 tokens (best) | 131,072 tokens | 30,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | Yes |
| PDFs | No | No | Yes |
| Audio | No | No | No |
| Video | Yes | No | No |
| Reasoning | Yes | Yes | Yeslow · medium · high |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | Yes |
| Availability | |||
| Weights | Open | Open | Proprietary |
| API model ID | kimi-k2.7-code | glm-5 | grok-4.3 |
| API providers | 51 (best) | 27 | 27 |
| Released | Jun 12, 2026 | Feb 12, 2026 | Apr 17, 2026 |
| Knowledge cutoff | Jan 2025 | — | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Kimi K2.7 Code$17.50
- GLM-5$16.40
- Grok 4.3$17.50
Which should you choose?
Which is better: Kimi K2.7 Code, GLM-5 or Grok 4.3?
It is close. Our weighted score puts them within 2 points (Grok 4.3 67/100, Kimi K2.7 Code 64/100, GLM-5 55/100), so choose by what matters most for your work: Kimi K2.7 Code for raw capability, GLM-5 on price and Grok 4.3 for long inputs. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Kimi K2.7 Code, GLM-5 or Grok 4.3?
GLM-5 is cheaper at $1.00 input / $3.20 output per million tokens (official Z.AI API price). Grok 4.3 costs $1.25 input / $2.50 output per million tokens (official xAI API price); Kimi K2.7 Code costs $0.95 input / $4.00 output per million tokens (official Moonshot AI API price). At a typical mix of three input tokens to one output token, that is $1.55 per million tokens for GLM-5 versus $1.56 for Grok 4.3 (1× as much) and $1.71 for Kimi K2.7 Code (1.1× 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), Grok 4.3 149.2 (#55 of 148) and GLM-5 145.8 (#74 of 148). The confidence ranges of the top two overlap (148.1–151.8 vs 147.6–150.8), so treat the gap as small. On individual benchmarks: GPQA Diamond — Grok 4.3 88.8%, Kimi K2.7 Code 87.9%, GLM-5 87.8%; OTIS Mock AIME 2024–2025 — Kimi K2.7 Code 95.6%, Grok 4.3 93.3%, GLM-5 80.0%.
Which is better for coding?
There are no published SWE-bench Verified results for Kimi K2.7 Code and Grok 4.3 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?
Grok 4.3 has the largest context window at 1,000,000 tokens, against 262,144 for Kimi K2.7 Code and 204,800 for GLM-5. Maximum output per response: Kimi K2.7 Code up to 262,144, GLM-5 up to 131,072, Grok 4.3 up to 30,000 tokens.
Which can read images, PDFs, audio or video?
Kimi K2.7 Code accepts text, images and video; GLM-5 accepts text; Grok 4.3 accepts text, images and PDFs. Kimi K2.7 Code handles the widest range of inputs.
Are any of these open source?
Kimi K2.7 Code and GLM-5 publishes its weights and can be self-hosted; Grok 4.3 is proprietary.
Which is newer?
Kimi K2.7 Code is the newest, released Jun 12, 2026. Grok 4.3 came out Apr 17, 2026; GLM-5 came out Feb 12, 2026. Knowledge cutoff: Kimi K2.7 Code Jan 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.
