MiniMax-M2 Her vs Qwen3 Coder Next vs GLM-4.6V
GLM-4.6V comes out ahead, 59 to 51 and 45 on our weighted score.
MiniMaxMiniMax-M2 Her
45/100- ECI—
- Price$0.30 / $1.20
- Context66K
Alibaba (Qwen)Qwen3 Coder Next
51/100- ECI—
- Price$0.20 / $1.20
- Context262K
- Our pick
Z.ai (Zhipu)GLM-4.6V
59/100- ECI—
- Price$0.30 / $0.90
- Context128K
GLM-4.6V is our pick
GLM-4.6V is the better all-round choice, scoring 59/100 against Qwen3 Coder Next (51) and MiniMax-M2 Her (45). It leads on inputs & features. Qwen3 Coder Next wins on 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 priceQwen3 Coder Next and GLM-4.6VQwen3 Coder Next $0.45 · GLM-4.6V $0.45 · MiniMax-M2 Her $0.525 per 1M tokens (3:1 blend)
- Longest contextQwen3 Coder NextQwen3 Coder Next 262,144 · GLM-4.6V 128,000 · MiniMax-M2 Her 65,536 tokens
- Widest inputsGLM-4.6VMiniMax-M2 Her: Text · Qwen3 Coder Next: Text · GLM-4.6V: Text, Images, Video
- Self-hostingQwen3 Coder Next and GLM-4.6VPublishes downloadable weights
| Measure | Weight | MiniMax-M2 Her | Qwen3 Coder Next | GLM-4.6V |
|---|---|---|---|---|
| Price | 50% | 63 | 66 | 66 |
| Inputs & features | 30% | 35 | 35 | 70 |
| Context window | 20% | 12 | 37 | 24 |
| Overall | 100% | 45/100 | 51/100 | 59/100 |
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 | MiniMax-M2 HerMiniMax | Qwen3 Coder NextAlibaba (Qwen) | GLM-4.6VZ.ai (Zhipu) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.30 | $0.20 (best) | $0.30 |
| Output | $1.20 | $1.20 | $0.90 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.525 | $0.45 (best) | $0.45 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 4 providers | Median of 11 providers | Official Z.AI API |
| Limits | |||
| Context window | 65,536 tokens | 262,144 tokens (best) | 128,000 tokens |
| Max output | 2,048 tokens | 65,536 tokens (best) | 32,768 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | Yes |
| Reasoning | Yes | No | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | Proprietary | Open | Open |
| API model ID | — | — | glm-4.6v |
| API providers | 4 | 11 (best) | 10 |
| Released | Jan 23, 2026 | Feb 3, 2026 | Dec 8, 2025 |
| Knowledge cutoff | — | Sep 2025 | 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.
- MiniMax-M2 Her$5.40
- Qwen3 Coder Next$4.40
- GLM-4.6V$4.80
Which should you choose?
Which is better: MiniMax-M2 Her, Qwen3 Coder Next or GLM-4.6V?
GLM-4.6V is the better all-round choice, scoring 59/100 against Qwen3 Coder Next (51) and MiniMax-M2 Her (45). It leads on inputs & features. Qwen3 Coder Next wins on 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, MiniMax-M2 Her, Qwen3 Coder Next or GLM-4.6V?
Qwen3 Coder Next is cheaper at $0.20 input / $1.20 output per million tokens (median across 11 API providers). GLM-4.6V costs $0.30 input / $0.90 output per million tokens (official Z.AI API price); MiniMax-M2 Her costs $0.30 input / $1.20 output per million tokens (median across 4 API providers). At a typical mix of three input tokens to one output token, that is $0.45 per million tokens for Qwen3 Coder Next versus $0.45 for GLM-4.6V (1× as much) and $0.525 for MiniMax-M2 Her (1.2× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. MiniMax-M2 Her has not been scored yet, Qwen3 Coder Next has not been scored yet and GLM-4.6V has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for MiniMax-M2 Her, Qwen3 Coder Next and GLM-4.6V 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?
Qwen3 Coder Next has the largest context window at 262,144 tokens, against 128,000 for GLM-4.6V and 65,536 for MiniMax-M2 Her. Maximum output per response: MiniMax-M2 Her up to 2,048, Qwen3 Coder Next up to 65,536, GLM-4.6V up to 32,768 tokens.
Which can read images, PDFs, audio or video?
MiniMax-M2 Her accepts text; Qwen3 Coder Next accepts text; GLM-4.6V accepts text, images and video. GLM-4.6V handles the widest range of inputs.
Are any of these open source?
Qwen3 Coder Next and GLM-4.6V publishes its weights and can be self-hosted; MiniMax-M2 Her is proprietary.
Which is newer?
Qwen3 Coder Next is the newest, released Feb 3, 2026. MiniMax-M2 Her came out Jan 23, 2026; GLM-4.6V came out Dec 8, 2025. Knowledge cutoff: Qwen3 Coder Next Sep 2025, GLM-4.6V 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.
