Kimi K2.6 vs GLM-5-Turbo vs Grok 4.20 (Reasoning)
Grok 4.20 (Reasoning) comes out ahead, 56 to 51 and 38 on our weighted score, and it is the cheaper option too.
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
- Our pick
xAIGrok 4.20 (Reasoning)
56/100- ECI152.0
- Price$1.25 / $2.50
- Context1M
Grok 4.20 (Reasoning) is our pick
Grok 4.20 (Reasoning) is the better all-round choice, scoring 56/100 against Kimi K2.6 (51) and GLM-5-Turbo (38). It leads on price 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 priceGrok 4.20 (Reasoning)Grok 4.20 (Reasoning) $1.56 · Kimi K2.6 $1.71 · GLM-5-Turbo $1.90 per 1M tokens (3:1 blend)
- Longest contextGrok 4.20 (Reasoning)Grok 4.20 (Reasoning) 1,000,000 · Kimi K2.6 262,144 · GLM-5-Turbo 200,000 tokens
- Widest inputsKimi K2.6 and Grok 4.20 (Reasoning)Kimi K2.6: Text, Images, Video · GLM-5-Turbo: Text · Grok 4.20 (Reasoning): Text, Images, PDFs
- Self-hostingKimi K2.6Publishes downloadable weights
| Measure | Weight | Kimi K2.6 | GLM-5-Turbo | Grok 4.20 (Reasoning) |
|---|---|---|---|---|
| Price | 50% | 39 | 37 | 41 |
| Inputs & features | 30% | 80 | 45 | 80 |
| Context window | 20% | 37 | 32 | 60 |
| Overall | 100% | 51/100 | 38/100 | 56/100 |
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) | Grok 4.20 (Reasoning)xAI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 151.1 | — | 152.0 (best) |
| ECI rank | #45 of 148 | — | #41 of 148 (best) |
| GPQA DiamondGraduate-level science questions | 90.8% (best) | — | 89.3% |
| FrontierMath Tiers 1–3Research-level mathematics | 57.2% (best) | — | 44.9% |
| OTIS Mock AIME 2024–2025Competition mathematics | 96.1% (best) | — | 92.2% |
| SWE-bench VerifiedFixing real GitHub issues | 76.7% | — | — |
| SimpleQA VerifiedShort factual questions | 34.9% (best) | — | 30.2% |
| Price per million tokens | |||
| Input | $0.95 (best) | $1.20 | $1.25 |
| Output | $4.00 | $4.00 | $2.50 (best) |
| Cached input | $0.16 (best) | $0.24 | $0.20 |
| Blended (3:1) | $1.71 | $1.90 | $1.56 (best) |
| 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 | 200,000 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 | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | Yes | Yes |
| Availability | |||
| Weights | Open | Proprietary | Proprietary |
| API model ID | kimi-k2.6 | glm-5-turbo | grok-4.20-0309-reasoning |
| API providers | 46 (best) | 17 | 11 |
| Released | Apr 21, 2026 | Mar 16, 2026 | Mar 9, 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
- Grok 4.20 (Reasoning)$17.50
Which should you choose?
Which is better: Kimi K2.6, GLM-5-Turbo or Grok 4.20 (Reasoning)?
Grok 4.20 (Reasoning) is the better all-round choice, scoring 56/100 against Kimi K2.6 (51) and GLM-5-Turbo (38). It leads on price 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, GLM-5-Turbo or Grok 4.20 (Reasoning)?
Grok 4.20 (Reasoning) is cheaper at $1.25 input / $2.50 output per million tokens (official xAI API price). Kimi K2.6 costs $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 $1.56 per million tokens for Grok 4.20 (Reasoning) versus $1.71 for Kimi K2.6 (1.1× as much) and $1.90 for GLM-5-Turbo (1.2× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Kimi K2.6 has an ECI of 151.1, GLM-5-Turbo has not been scored yet and Grok 4.20 (Reasoning) has an ECI of 152.0.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-5-Turbo and Grok 4.20 (Reasoning) yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. All three support tool calling for agent workflows.
Which has the bigger context window?
Grok 4.20 (Reasoning) has the largest context window at 1,000,000 tokens, against 262,144 for Kimi K2.6 and 200,000 for GLM-5-Turbo. Maximum output per response: Kimi K2.6 up to 262,144, GLM-5-Turbo up to 131,072, Grok 4.20 (Reasoning) up to 30,000 tokens.
Which can read images, PDFs, audio or video?
Kimi K2.6 accepts text, images and video; GLM-5-Turbo accepts text; Grok 4.20 (Reasoning) accepts text, images and PDFs. 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 and Grok 4.20 (Reasoning) is proprietary.
Which is newer?
Kimi K2.6 is the newest, released Apr 21, 2026. GLM-5-Turbo came out Mar 16, 2026; Grok 4.20 (Reasoning) came out Mar 9, 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.
