Kimi K2.6 vs GLM-5-Turbo
Kimi K2.6 comes out ahead, 51 to 38 on our weighted score, and it is the cheaper option too.
- Our pick
Moonshot AIKimi K2.6
51/100- ECI151.1
- Price$0.95 / $4.00
- Context262K
Z.ai (Zhipu)GLM-5-Turbo
38/100- ECI—
- Price$1.20 / $4.00
- Context200K
Add a model
Make it a three-way comparison.
Kimi K2.6 is our pick
Kimi K2.6 is the better all-round choice, scoring 51/100 against GLM-5-Turbo (38). It leads on price, inputs & features and context window. 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 K2.6Kimi K2.6 $1.71 · GLM-5-Turbo $1.90 per 1M tokens (3:1 blend)
- Longest contextKimi K2.6Kimi K2.6 262,144 · GLM-5-Turbo 200,000 tokens
- Widest inputsKimi K2.6Kimi K2.6: Text, Images, Video · GLM-5-Turbo: Text
- Self-hostingKimi K2.6Publishes downloadable weights
| Measure | Weight | Kimi K2.6 | GLM-5-Turbo |
|---|---|---|---|
| Price | 50% | 39 | 37 |
| Inputs & features | 30% | 80 | 45 |
| Context window | 20% | 37 | 32 |
| Overall | 100% | 51/100 | 38/100 |
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 | Kimi K2.6Moonshot AI | GLM-5-TurboZ.ai (Zhipu) |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | 151.1 | — |
| ECI rank | #45 of 148 | — |
| GPQA DiamondGraduate-level science questions | 90.8% | — |
| FrontierMath Tiers 1–3Research-level mathematics | 57.2% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 96.1% | — |
| SWE-bench VerifiedFixing real GitHub issues | 76.7% | — |
| SimpleQA VerifiedShort factual questions | 34.9% | — |
| Price per million tokens | ||
| Input | $0.95 (best) | $1.20 |
| Output | $4.00 | $4.00 |
| Cached input | $0.16 (best) | $0.24 |
| Blended (3:1) | $1.71 (best) | $1.90 |
| Long-context rate | Same rate | Same rate |
| Price source | Official Moonshot AI API | Official Z.AI API |
| Limits | ||
| Context window | 262,144 tokens (best) | 200,000 tokens |
| Max output | 262,144 tokens (best) | 131,072 tokens |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | Yes | No |
| PDFs | No | No |
| Audio | No | No |
| Video | Yes | No |
| Reasoning | Yes | Yes |
| Tool calling | Yes | Yes |
| Structured output | Yes | Yes |
| Availability | ||
| Weights | Open | Proprietary |
| API model ID | kimi-k2.6 | glm-5-turbo |
| API providers | 46 (best) | 17 |
| Released | Apr 21, 2026 | Mar 16, 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 K2.6$17.50
- GLM-5-Turbo$20.00
Which should you choose?
Which is better: Kimi K2.6 or GLM-5-Turbo?
Kimi K2.6 is the better all-round choice, scoring 51/100 against GLM-5-Turbo (38). It leads on price, inputs & features and context window. 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, Kimi K2.6 or GLM-5-Turbo?
Kimi K2.6 is cheaper at $0.95 input / $4.00 output per million tokens (official Moonshot AI API price). GLM-5-Turbo costs $1.20 input / $4.00 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 $1.90 for GLM-5-Turbo (1.1× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers both models yet. Kimi K2.6 has an ECI of 151.1 and GLM-5-Turbo has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-5-Turbo 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?
Kimi K2.6 has the largest context window at 262,144 tokens, against 200,000 for GLM-5-Turbo. Maximum output per response: Kimi K2.6 up to 262,144, GLM-5-Turbo up to 131,072 tokens.
Which can read images, PDFs, audio or video?
Kimi K2.6 accepts text, images and video; GLM-5-Turbo accepts text. Kimi K2.6 handles the widest range of inputs.
Are any of these open source?
Kimi K2.6 publishes its weights and can be self-hosted; GLM-5-Turbo is proprietary.
Which is newer?
Kimi K2.6 is the newest, released Apr 21, 2026. GLM-5-Turbo came out Mar 16, 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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