GLM-4.7 vs MiniMax-M2.5-highspeed vs Kimi K2 Thinking
Too close to call on our weighted score (Kimi K2 Thinking 42, GLM-4.7 42, MiniMax-M2.5-highspeed 41). The right pick depends on what you value most.
Z.ai (Zhipu)GLM-4.7
42/100- ECI143.5
- Price$0.60 / $2.20
- Context205K
MiniMaxMiniMax-M2.5-highspeed
41/100- ECI—
- Price$0.60 / $2.40
- Context205K
Moonshot AIKimi K2 Thinking
42/100- ECI146.0
- Price$0.60 / $2.50
- Context262K
Too close to call
It is close. Our weighted score puts them within a point (Kimi K2 Thinking 42/100, GLM-4.7 42/100, MiniMax-M2.5-highspeed 41/100), so choose by what matters most for your work: GLM-4.7 on price and Kimi K2 Thinking 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 priceGLM-4.7GLM-4.7 $1.00 · MiniMax-M2.5-highspeed $1.05 · Kimi K2 Thinking $1.07 per 1M tokens (3:1 blend)
- Longest contextKimi K2 ThinkingKimi K2 Thinking 262,144 · GLM-4.7 204,800 · MiniMax-M2.5-highspeed 204,800 tokens
- Widest inputsSame inputsGLM-4.7: Text · MiniMax-M2.5-highspeed: Text · Kimi K2 Thinking: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | GLM-4.7 | MiniMax-M2.5-highspeed | Kimi K2 Thinking |
|---|---|---|---|---|
| Price | 50% | 50 | 49 | 48 |
| Inputs & features | 30% | 35 | 35 | 35 |
| Context window | 20% | 32 | 32 | 37 |
| Overall | 100% | 42/100 | 41/100 | 42/100 |
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-4.7Z.ai (Zhipu) | MiniMax-M2.5-highspeedMiniMax | Kimi K2 ThinkingMoonshot AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 143.5 | — | 146.0 (best) |
| ECI rank | #84 of 148 | — | #72 of 148 (best) |
| GPQA DiamondGraduate-level science questions | 83.3% | — | 84.2% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | 83.3% (best) | — | 83.1% |
| SimpleQA VerifiedShort factual questions | 32.2% | — | — |
| Price per million tokens | |||
| Input | $0.60 | $0.60 | $0.60 |
| Output | $2.20 (best) | $2.40 | $2.50 |
| Cached input | $0.11 | $0.06 (best) | — |
| Blended (3:1) | $1.00 (best) | $1.05 | $1.07 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Official MiniMax (minimax.io) API | Median of 10 providers |
| Limits | |||
| Context window | 204,800 tokens | 204,800 tokens | 262,144 tokens (best) |
| Max output | 131,072 tokens | 131,072 tokens | 262,144 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | glm-4.7 | MiniMax-M2.5-highspeed | — |
| API providers | 20 (best) | 7 | 10 |
| Released | Dec 22, 2025 | Feb 13, 2026 | Nov 6, 2025 |
| Knowledge cutoff | Apr 2025 | — | Aug 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- GLM-4.7$10.40
- MiniMax-M2.5-highspeed$10.80
- Kimi K2 Thinking$11.00
Which should you choose?
Which is better: GLM-4.7, MiniMax-M2.5-highspeed or Kimi K2 Thinking?
It is close. Our weighted score puts them within a point (Kimi K2 Thinking 42/100, GLM-4.7 42/100, MiniMax-M2.5-highspeed 41/100), so choose by what matters most for your work: GLM-4.7 on price and Kimi K2 Thinking 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-4.7, MiniMax-M2.5-highspeed or Kimi K2 Thinking?
GLM-4.7 is cheaper at $0.60 input / $2.20 output per million tokens (official Z.AI API price). MiniMax-M2.5-highspeed costs $0.60 input / $2.40 output per million tokens (official MiniMax (minimax.io) API price); Kimi K2 Thinking costs $0.60 input / $2.50 output per million tokens (median across 10 API providers). At a typical mix of three input tokens to one output token, that is $1.00 per million tokens for GLM-4.7 versus $1.05 for MiniMax-M2.5-highspeed (1.1× as much) and $1.07 for Kimi K2 Thinking (1.1× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GLM-4.7 has an ECI of 143.5, MiniMax-M2.5-highspeed has not been scored yet and Kimi K2 Thinking has an ECI of 146.0.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-4.7, MiniMax-M2.5-highspeed and Kimi K2 Thinking 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?
Kimi K2 Thinking has the largest context window at 262,144 tokens, against 204,800 for GLM-4.7 and 204,800 for MiniMax-M2.5-highspeed. Maximum output per response: GLM-4.7 up to 131,072, MiniMax-M2.5-highspeed up to 131,072, Kimi K2 Thinking up to 262,144 tokens.
Which can read images, PDFs, audio or video?
GLM-4.7 accepts text; MiniMax-M2.5-highspeed accepts text; Kimi K2 Thinking accepts text. They handle the same number of input types.
Are any of these open source?
Yes, all three publish their weights, so you can self-host them.
Which is newer?
MiniMax-M2.5-highspeed is the newest, released Feb 13, 2026. GLM-4.7 came out Dec 22, 2025; Kimi K2 Thinking came out Nov 6, 2025. Knowledge cutoff: GLM-4.7 Apr 2025, Kimi K2 Thinking Aug 2024.
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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