GLM-5.1 vs MiniMax-M2.7 vs Qwen3.6 Max Preview
MiniMax-M2.7 comes out ahead, 61 to 57 and 54 on our weighted score, and it is the cheaper option too.
Z.ai (Zhipu)GLM-5.1
57/100- ECI149.9
- Price$1.40 / $4.40
- Context200K
- Our pick
MiniMaxMiniMax-M2.7
61/100- ECI145.9
- Price$0.30 / $1.20
- Context205K
Alibaba (Qwen)Qwen3.6 Max Preview
54/100- ECI149.2
- Price$1.30 / $7.80
- Context262K
MiniMax-M2.7 is our pick
MiniMax-M2.7 is the better all-round choice, scoring 61/100 against GLM-5.1 (57) and Qwen3.6 Max Preview (54). It leads on price. GLM-5.1 wins on inputs & features. Qwen3.6 Max Preview wins on context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityGLM-5.1Capabilities Index (ECI): GLM-5.1 149.9 · Qwen3.6 Max Preview 149.2 · MiniMax-M2.7 145.9
- Lowest priceMiniMax-M2.7MiniMax-M2.7 $0.525 · GLM-5.1 $2.15 · Qwen3.6 Max Preview $2.92 per 1M tokens (3:1 blend)
- Longest contextQwen3.6 Max PreviewQwen3.6 Max Preview 262,144 · MiniMax-M2.7 204,800 · GLM-5.1 200,000 tokens
- Widest inputsSame inputsGLM-5.1: Text · MiniMax-M2.7: Text · Qwen3.6 Max Preview: Text
- Self-hostingGLM-5.1 and MiniMax-M2.7Publishes downloadable weights
| Measure | Weight | GLM-5.1 | MiniMax-M2.7 | Qwen3.6 Max Preview |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 78 | 73 | 77 |
| Price | 25% | 34 | 63 | 28 |
| Inputs & features | 15% | 45 | 35 | 35 |
| Context window | 10% | 32 | 32 | 37 |
| Overall | 100% | 57/100 | 61/100 | 54/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GLM-5.1Z.ai (Zhipu) | MiniMax-M2.7MiniMax | Qwen3.6 Max PreviewAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 149.9 (best) | 145.9 | 149.2 |
| ECI rank | #51 of 148 (best) | #73 of 148 | #54 of 148 |
| GPQA DiamondGraduate-level science questions | 89.9% (best) | — | 87.4% |
| FrontierMath Tiers 1–3Research-level mathematics | 36.8% | — | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 93.3% (best) | — | 91.1% |
| SWE-bench VerifiedFixing real GitHub issues | 74.2% | — | 76.7% (best) |
| SimpleQA VerifiedShort factual questions | 34.0% | — | 52.0% (best) |
| Price per million tokens | |||
| Input | $1.40 | $0.30 (best) | $1.30 |
| Output | $4.40 | $1.20 (best) | $7.80 |
| Cached input | $0.26 | $0.06 (best) | $0.13 |
| Blended (3:1) | $2.15 | $0.525 (best) | $2.92 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Official MiniMax (minimax.io) API | Official Alibaba API |
| Limits | |||
| Context window | 200,000 tokens | 204,800 tokens | 262,144 tokens (best) |
| Max output | 131,072 tokens (best) | 131,072 tokens (best) | 65,536 tokens |
| 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 | Yes | No | No |
| Availability | |||
| Weights | Open | Open | Proprietary |
| API model ID | glm-5.1 | MiniMax-M2.7 | qwen3.6-max-preview |
| API providers | 40 (best) | 29 | 10 |
| Released | Apr 7, 2026 | Mar 18, 2026 | Apr 20, 2026 |
| Knowledge cutoff | — | — | Apr 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-5.1$22.80
- MiniMax-M2.7$5.40
- Qwen3.6 Max Preview$28.60
Which should you choose?
Which is better: GLM-5.1, MiniMax-M2.7 or Qwen3.6 Max Preview?
MiniMax-M2.7 is the better all-round choice, scoring 61/100 against GLM-5.1 (57) and Qwen3.6 Max Preview (54). It leads on price. GLM-5.1 wins on inputs & features. Qwen3.6 Max Preview wins on context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, GLM-5.1, MiniMax-M2.7 or Qwen3.6 Max Preview?
MiniMax-M2.7 is cheaper at $0.30 input / $1.20 output per million tokens (official MiniMax (minimax.io) API price). GLM-5.1 costs $1.40 input / $4.40 output per million tokens (official Z.AI API price); Qwen3.6 Max Preview costs $1.30 input / $7.80 output per million tokens (official Alibaba API price). At a typical mix of three input tokens to one output token, that is $0.525 per million tokens for MiniMax-M2.7 versus $2.15 for GLM-5.1 (4.1× as much) and $2.92 for Qwen3.6 Max Preview (5.6× as much).
Which scores higher on benchmarks?
GLM-5.1 scores higher on the Capabilities Index (ECI): GLM-5.1 149.9 (#51 of 148), Qwen3.6 Max Preview 149.2 (#54 of 148) and MiniMax-M2.7 145.9 (#73 of 148). The confidence ranges of the top two overlap (148.0–151.6 vs 147.6–152.0), so treat the gap as small.
Which is better for coding?
There are no published SWE-bench Verified results for MiniMax-M2.7 yet, so there is no like-for-like coding score. On overall capability, GLM-5.1 leads, which tends to carry over to coding, but test on your own codebase. All three support tool calling for agent workflows.
Which has the bigger context window?
Qwen3.6 Max Preview has the largest context window at 262,144 tokens, against 204,800 for MiniMax-M2.7 and 200,000 for GLM-5.1. Maximum output per response: GLM-5.1 up to 131,072, MiniMax-M2.7 up to 131,072, Qwen3.6 Max Preview up to 65,536 tokens.
Which can read images, PDFs, audio or video?
GLM-5.1 accepts text; MiniMax-M2.7 accepts text; Qwen3.6 Max Preview accepts text. They handle the same number of input types.
Are any of these open source?
GLM-5.1 and MiniMax-M2.7 publishes its weights and can be self-hosted; Qwen3.6 Max Preview is proprietary.
Which is newer?
Qwen3.6 Max Preview is the newest, released Apr 20, 2026. GLM-5.1 came out Apr 7, 2026; MiniMax-M2.7 came out Mar 18, 2026. Knowledge cutoff: Qwen3.6 Max Preview Apr 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.
