GLM-4.6V vs Mercury 2 vs Qwen3 Coder Next
GLM-4.6V comes out ahead, 59 to 53 and 51 on our weighted score, though Mercury 2 is 17% cheaper per token.
- Our pick
Z.ai (Zhipu)GLM-4.6V
59/100- ECI—
- Price$0.30 / $0.90
- Context128K
InceptionMercury 2
53/100- ECI—
- Price$0.25 / $0.75
- Context128K
Alibaba (Qwen)Qwen3 Coder Next
51/100- ECI—
- Price$0.20 / $1.20
- Context262K
GLM-4.6V is our pick
GLM-4.6V is the better all-round choice, scoring 59/100 against Mercury 2 (53) and Qwen3 Coder Next (51). It leads on inputs & features. Mercury 2 wins on price. 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 priceMercury 2Mercury 2 $0.375 · GLM-4.6V $0.45 · Qwen3 Coder Next $0.45 per 1M tokens (3:1 blend)
- Longest contextQwen3 Coder NextQwen3 Coder Next 262,144 · GLM-4.6V 128,000 · Mercury 2 128,000 tokens
- Widest inputsGLM-4.6VGLM-4.6V: Text, Images, Video · Mercury 2: Text · Qwen3 Coder Next: Text
- Self-hostingGLM-4.6V and Qwen3 Coder NextPublishes downloadable weights
| Measure | Weight | GLM-4.6V | Mercury 2 | Qwen3 Coder Next |
|---|---|---|---|---|
| Price | 50% | 66 | 70 | 66 |
| Inputs & features | 30% | 70 | 45 | 35 |
| Context window | 20% | 24 | 24 | 37 |
| Overall | 100% | 59/100 | 53/100 | 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-4.6VZ.ai (Zhipu) | Mercury 2Inception | Qwen3 Coder NextAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.30 | $0.25 | $0.20 (best) |
| Output | $0.90 | $0.75 (best) | $1.20 |
| Cached input | — | $0.025 | — |
| Blended (3:1) | $0.45 | $0.375 (best) | $0.45 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Official Inception API | Median of 11 providers |
| Limits | |||
| Context window | 128,000 tokens | 128,000 tokens | 262,144 tokens (best) |
| Max output | 32,768 tokens | 50,000 tokens | 65,536 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | Yes | No | No |
| Reasoning | Yes | Yeslow · medium · high | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | Yes |
| Availability | |||
| Weights | Open | Proprietary | Open |
| API model ID | glm-4.6v | mercury-2 | — |
| API providers | 10 | 1 | 11 (best) |
| Released | Dec 8, 2025 | Feb 24, 2026 | Feb 3, 2026 |
| Knowledge cutoff | Apr 2025 | Jan 1, 2025 | Sep 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-4.6V$4.80
- Mercury 2$4.00
- Qwen3 Coder Next$4.40
Which should you choose?
Which is better: GLM-4.6V, Mercury 2 or Qwen3 Coder Next?
GLM-4.6V is the better all-round choice, scoring 59/100 against Mercury 2 (53) and Qwen3 Coder Next (51). It leads on inputs & features. Mercury 2 wins on price. 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, GLM-4.6V, Mercury 2 or Qwen3 Coder Next?
Mercury 2 is cheaper at $0.25 input / $0.75 output per million tokens (official Inception API price). GLM-4.6V costs $0.30 input / $0.90 output per million tokens (official Z.AI API price); Qwen3 Coder Next costs $0.20 input / $1.20 output per million tokens (median across 11 API providers). At a typical mix of three input tokens to one output token, that is $0.375 per million tokens for Mercury 2 versus $0.45 for GLM-4.6V (1.2× as much) and $0.45 for Qwen3 Coder Next (1.2× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GLM-4.6V has not been scored yet, Mercury 2 has not been scored yet and Qwen3 Coder Next has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-4.6V, Mercury 2 and Qwen3 Coder Next 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 128,000 for Mercury 2. Maximum output per response: GLM-4.6V up to 32,768, Mercury 2 up to 50,000, Qwen3 Coder Next up to 65,536 tokens.
Which can read images, PDFs, audio or video?
GLM-4.6V accepts text, images and video; Mercury 2 accepts text; Qwen3 Coder Next accepts text. GLM-4.6V handles the widest range of inputs.
Are any of these open source?
GLM-4.6V and Qwen3 Coder Next publishes its weights and can be self-hosted; Mercury 2 is proprietary.
Which is newer?
Mercury 2 is the newest, released Feb 24, 2026. Qwen3 Coder Next came out Feb 3, 2026; GLM-4.6V came out Dec 8, 2025. Knowledge cutoff: GLM-4.6V Apr 2025, Mercury 2 Jan 1, 2025, Qwen3 Coder Next Sep 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.
