GLM-5-Turbo vs Grok 4.20 Multi-Agent vs Kimi K2.6
Too close to call on our weighted score (Kimi K2.6 51, Grok 4.20 Multi-Agent 49, GLM-5-Turbo 38). The right pick depends on what you value most.
Z.ai (Zhipu)GLM-5-Turbo
38/100- ECI—
- Price$1.20 / $4.00
- Context200K
xAIGrok 4.20 Multi-Agent
49/100- ECI—
- Price$1.25 / $2.50
- Context1M
Moonshot AIKimi K2.6
51/100- ECI151.1
- Price$0.95 / $4.00
- Context262K
Too close to call
It is close. Our weighted score puts them within 2 points (Kimi K2.6 51/100, Grok 4.20 Multi-Agent 49/100, GLM-5-Turbo 38/100), so choose by what matters most for your work: Grok 4.20 Multi-Agent on price and Grok 4.20 Multi-Agent for long inputs. 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 Multi-AgentGrok 4.20 Multi-Agent $1.56 · Kimi K2.6 $1.71 · GLM-5-Turbo $1.90 per 1M tokens (3:1 blend)
- Longest contextGrok 4.20 Multi-AgentGrok 4.20 Multi-Agent 1,000,000 · Kimi K2.6 262,144 · GLM-5-Turbo 200,000 tokens
- Widest inputsGrok 4.20 Multi-Agent and Kimi K2.6GLM-5-Turbo: Text · Grok 4.20 Multi-Agent: Text, Images, PDFs · Kimi K2.6: Text, Images, Video
- Self-hostingKimi K2.6Publishes downloadable weights
| Measure | Weight | GLM-5-Turbo | Grok 4.20 Multi-Agent | Kimi K2.6 |
|---|---|---|---|---|
| Price | 50% | 37 | 41 | 39 |
| Inputs & features | 30% | 45 | 55 | 80 |
| Context window | 20% | 32 | 60 | 37 |
| Overall | 100% | 38/100 | 49/100 | 51/100 |
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 | GLM-5-TurboZ.ai (Zhipu) | Grok 4.20 Multi-AgentxAI | Kimi K2.6Moonshot AI |
|---|---|---|---|
| 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 | $1.20 | $1.25 | $0.95 (best) |
| Output | $4.00 | $2.50 (best) | $4.00 |
| Cached input | $0.24 | $0.20 | $0.16 (best) |
| Blended (3:1) | $1.90 | $1.56 (best) | $1.71 |
| Long-context rate | Same rate | Over 200K: $2.50 / $5.00 | Same rate |
| Price source | Official Z.AI API | Official xAI API | Official Moonshot AI API |
| Limits | |||
| Context window | 200,000 tokens | 1,000,000 tokens (best) | 262,144 tokens |
| Max output | 131,072 tokens | 30,000 tokens | 262,144 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | Yes | No |
| Audio | No | No | No |
| Video | No | No | Yes |
| Reasoning | Yes | Yeslow · medium · high · xhigh | Yes |
| Tool calling | Yes | No | Yes |
| Structured output | Yes | Yes | Yes |
| Availability | |||
| Weights | Proprietary | Proprietary | Open |
| API model ID | glm-5-turbo | grok-4.20-multi-agent-0309 | kimi-k2.6 |
| API providers | 17 | 1 | 46 (best) |
| Released | Mar 16, 2026 | Mar 9, 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-Turbo$20.00
- Grok 4.20 Multi-Agent$17.50
- Kimi K2.6$17.50
Which should you choose?
Which is better: GLM-5-Turbo, Grok 4.20 Multi-Agent or Kimi K2.6?
It is close. Our weighted score puts them within 2 points (Kimi K2.6 51/100, Grok 4.20 Multi-Agent 49/100, GLM-5-Turbo 38/100), so choose by what matters most for your work: Grok 4.20 Multi-Agent on price and Grok 4.20 Multi-Agent for long inputs. 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, GLM-5-Turbo, Grok 4.20 Multi-Agent or Kimi K2.6?
Grok 4.20 Multi-Agent 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 Multi-Agent 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. GLM-5-Turbo has not been scored yet, Grok 4.20 Multi-Agent has not been scored yet and Kimi K2.6 has an ECI of 151.1.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-5-Turbo and Grok 4.20 Multi-Agent yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Grok 4.20 Multi-Agent does not support tool calling, which most coding agents need.
Which has the bigger context window?
Grok 4.20 Multi-Agent 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: GLM-5-Turbo up to 131,072, Grok 4.20 Multi-Agent up to 30,000, Kimi K2.6 up to 262,144 tokens.
Which can read images, PDFs, audio or video?
GLM-5-Turbo accepts text; Grok 4.20 Multi-Agent accepts text, images and PDFs; Kimi K2.6 accepts text, images and video. Grok 4.20 Multi-Agent 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 Multi-Agent 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 Multi-Agent 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.
