GLM-5.2 vs Kimi K2.6
Kimi K2.6 comes out ahead, 65 to 61 on our weighted score, and it is the cheaper option too.
Kimi K2.6 is our pick
Kimi K2.6 is the better all-round choice, scoring 65/100 against GLM-5.2 (61). It leads on price and inputs & features. GLM-5.2 wins on context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityGLM-5.2Capabilities Index (ECI): GLM-5.2 151.8 · Kimi K2.6 151.1
- Lowest priceKimi K2.6Kimi K2.6 $1.71 · GLM-5.2 $2.15 per 1M tokens (3:1 blend)
- Longest contextGLM-5.2GLM-5.2 1,000,000 · Kimi K2.6 262,144 tokens
- Widest inputsKimi K2.6GLM-5.2: Text · Kimi K2.6: Text, Images, Video
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | GLM-5.2 | Kimi K2.6 |
|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 80 | 79 |
| Price | 25% | 34 | 39 |
| Inputs & features | 15% | 45 | 80 |
| Context window | 10% | 60 | 37 |
| Overall | 100% | 61/100 | 65/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification |  GLM-5.2Z.ai (Zhipu) |  Kimi K2.6Moonshot AI |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | 151.8 (best) | 151.1 |
| ECI rank | #44 of 148 (best) | #45 of 148 |
| GPQA DiamondGraduate-level science questions | 91.9% (best) | 90.8% |
| FrontierMath Tiers 1–3Research-level mathematics | 59.2% (best) | 57.2% |
| OTIS Mock AIME 2024–2025Competition mathematics | 86.4% | 96.1% (best) |
| SWE-bench VerifiedFixing real GitHub issues | 78.7% (best) | 76.7% |
| SimpleQA VerifiedShort factual questions | 34.2% | 34.9% (best) |
| Price per million tokens | ||
| Input | $1.40 | $0.95 (best) |
| Output | $4.40 | $4.00 (best) |
| Cached input | $0.26 | $0.16 (best) |
| Blended (3:1) | $2.15 | $1.71 (best) |
| Long-context rate | Same rate | Same rate |
| Price source | Official Z.AI API | Official Moonshot AI API |
| Limits | ||
| Context window | 1,000,000 tokens (best) | 262,144 tokens |
| Max output | 131,072 tokens | 262,144 tokens (best) |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | No | Yes |
| PDFs | No | No |
| Audio | No | No |
| Video | No | Yes |
| Reasoning | Yeshigh · max | Yes |
| Tool calling | Yes | Yes |
| Structured output | Yes | Yes |
| Availability | ||
| Weights | Open | Open |
| API model ID | glm-5.2 | kimi-k2.6 |
| API providers | 80 (best) | 46 |
| Released | Jun 13, 2026 | Apr 21, 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.
- GLM-5.2$22.80
- Kimi K2.6$17.50
Which should you choose?
Which is better: GLM-5.2 or Kimi K2.6?
Kimi K2.6 is the better all-round choice, scoring 65/100 against GLM-5.2 (61). It leads on price and inputs & features. GLM-5.2 wins on context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, GLM-5.2 or Kimi K2.6?
Kimi K2.6 is cheaper at $0.95 input / $4.00 output per million tokens (official Moonshot AI API price). GLM-5.2 costs $1.40 input / $4.40 output per million tokens (official Z.AI API price). At a typical mix of three input tokens to one output token, that is $1.71 per million tokens for Kimi K2.6 versus $2.15 for GLM-5.2 (1.3× as much).
Which scores higher on benchmarks?
GLM-5.2 scores higher on the Capabilities Index (ECI): GLM-5.2 151.8 (#44 of 148) and Kimi K2.6 151.1 (#45 of 148). The confidence ranges of the top two overlap (149.8–154.0 vs 149.1–152.8), so treat the gap as small. On individual benchmarks: GPQA Diamond — GLM-5.2 91.9%, Kimi K2.6 90.8%; FrontierMath Tiers 1–3 — GLM-5.2 59.2%, Kimi K2.6 57.2%; OTIS Mock AIME 2024–2025 — Kimi K2.6 96.1%, GLM-5.2 86.4%; SWE-bench Verified — GLM-5.2 78.7%, Kimi K2.6 76.7%; SimpleQA Verified — Kimi K2.6 34.9%, GLM-5.2 34.2%.
Which is better for coding?
GLM-5.2 resolves more real GitHub issues on SWE-bench Verified: GLM-5.2 78.7% and Kimi K2.6 76.7%. Both support tool calling for agent workflows.
Which has the bigger context window?
GLM-5.2 has the largest context window at 1,000,000 tokens, against 262,144 for Kimi K2.6. Maximum output per response: GLM-5.2 up to 131,072, Kimi K2.6 up to 262,144 tokens.
Which can read images, PDFs, audio or video?
GLM-5.2 accepts text; Kimi K2.6 accepts text, images and video. Kimi K2.6 handles the widest range of inputs.
Are any of these open source?
Yes, both publish their weights, so you can self-host them.
Which is newer?
GLM-5.2 is the newest, released Jun 13, 2026. Kimi K2.6 came out Apr 21, 2026. Knowledge cutoff: Kimi K2.6 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.
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