Qwen3.6 27B vs MiniMax-M2.7 vs GLM-5
Qwen3.6 27B comes out ahead, 65 to 61 and 55 on our weighted score, though MiniMax-M2.7 is 2.6× cheaper per token.
- Our pick
Alibaba (Qwen)Qwen3.6 27B
65/100- ECI146.5
- Price$0.60 / $3.60
- Context262K
MiniMaxMiniMax-M2.7
61/100- ECI145.9
- Price$0.30 / $1.20
- Context205K
Z.ai (Zhipu)GLM-5
55/100- ECI145.8
- Price$1.00 / $3.20
- Context205K
Qwen3.6 27B is our pick
Qwen3.6 27B is the better all-round choice, scoring 65/100 against MiniMax-M2.7 (61) and GLM-5 (55). It leads on inputs & features and context window. MiniMax-M2.7 wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityQwen3.6 27BCapabilities Index (ECI): Qwen3.6 27B 146.5 · MiniMax-M2.7 145.9 · GLM-5 145.8
- Lowest priceMiniMax-M2.7MiniMax-M2.7 $0.525 · Qwen3.6 27B $1.35 · GLM-5 $1.55 per 1M tokens (3:1 blend)
- Longest contextQwen3.6 27BQwen3.6 27B 262,144 · MiniMax-M2.7 204,800 · GLM-5 204,800 tokens
- Widest inputsQwen3.6 27BQwen3.6 27B: Text, Images, Audio, Video · MiniMax-M2.7: Text · GLM-5: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Qwen3.6 27B | MiniMax-M2.7 | GLM-5 |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 74 | 73 | 73 |
| Price | 25% | 44 | 63 | 41 |
| Inputs & features | 15% | 90 | 35 | 35 |
| Context window | 10% | 37 | 32 | 32 |
| Overall | 100% | 65/100 | 61/100 | 55/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Qwen3.6 27BAlibaba (Qwen) | MiniMax-M2.7MiniMax | GLM-5Z.ai (Zhipu) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 146.5 (best) | 145.9 | 145.8 |
| ECI rank | #68 of 148 (best) | #73 of 148 | #74 of 148 |
| GPQA DiamondGraduate-level science questions | 85.9% | — | 87.8% (best) |
| FrontierMath Tiers 1–3Research-level mathematics | 35.1% | — | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 91.1% (best) | — | 80.0% |
| SWE-bench VerifiedFixing real GitHub issues | — | — | 72.1% |
| Price per million tokens | |||
| Input | $0.60 | $0.30 (best) | $1.00 |
| Output | $3.60 | $1.20 (best) | $3.20 |
| Cached input | — | $0.06 (best) | $0.20 |
| Blended (3:1) | $1.35 | $0.525 (best) | $1.55 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Alibaba API | Official MiniMax (minimax.io) API | Official Z.AI API |
| Limits | |||
| Context window | 262,144 tokens (best) | 204,800 tokens | 204,800 tokens |
| Max output | 65,536 tokens | 131,072 tokens (best) | 131,072 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | No |
| PDFs | No | No | No |
| Audio | Yes | No | No |
| Video | Yes | No | No |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | No |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | qwen3.6-27b | MiniMax-M2.7 | glm-5 |
| API providers | 27 | 29 (best) | 27 |
| Released | Apr 22, 2026 | Mar 18, 2026 | Feb 12, 2026 |
| Knowledge cutoff | — | — | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Qwen3.6 27B$13.20
- MiniMax-M2.7$5.40
- GLM-5$16.40
Which should you choose?
Which is better: Qwen3.6 27B, MiniMax-M2.7 or GLM-5?
Qwen3.6 27B is the better all-round choice, scoring 65/100 against MiniMax-M2.7 (61) and GLM-5 (55). It leads on inputs & features and context window. MiniMax-M2.7 wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Qwen3.6 27B, MiniMax-M2.7 or GLM-5?
MiniMax-M2.7 is cheaper at $0.30 input / $1.20 output per million tokens (official MiniMax (minimax.io) API price). Qwen3.6 27B costs $0.60 input / $3.60 output per million tokens (official Alibaba API price); GLM-5 costs $1.00 input / $3.20 output per million tokens (official Z.AI 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 $1.35 for Qwen3.6 27B (2.6× as much) and $1.55 for GLM-5 (3× as much).
Which scores higher on benchmarks?
Qwen3.6 27B scores higher on the Capabilities Index (ECI): Qwen3.6 27B 146.5 (#68 of 148), MiniMax-M2.7 145.9 (#73 of 148) and GLM-5 145.8 (#74 of 148). The confidence ranges of the top two overlap (144.2–147.9 vs 138.2–148.0), so treat the gap as small.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen3.6 27B and MiniMax-M2.7 yet, so there is no like-for-like coding score. On overall capability, Qwen3.6 27B 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 27B has the largest context window at 262,144 tokens, against 204,800 for MiniMax-M2.7 and 204,800 for GLM-5. Maximum output per response: Qwen3.6 27B up to 65,536, MiniMax-M2.7 up to 131,072, GLM-5 up to 131,072 tokens.
Which can read images, PDFs, audio or video?
Qwen3.6 27B accepts text, images, audio and video; MiniMax-M2.7 accepts text; GLM-5 accepts text. Qwen3.6 27B handles the widest range of inputs.
Are any of these open source?
Yes, all three publish their weights, so you can self-host them.
Which is newer?
Qwen3.6 27B is the newest, released Apr 22, 2026. MiniMax-M2.7 came out Mar 18, 2026; GLM-5 came out Feb 12, 2026.
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.
