GLM-4.6V vs Mercury 2
GLM-4.6V comes out ahead, 59 to 53 on our weighted score, though Mercury 2 is 17% cheaper per token.
GLM-4.6V is our pick
GLM-4.6V is the better all-round choice, scoring 59/100 against Mercury 2 (53). It leads on inputs & features. Mercury 2 wins on price. 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 per 1M tokens (3:1 blend)
- Longest contextAbout the sameGLM-4.6V 128,000 · Mercury 2 128,000 tokens
- Widest inputsGLM-4.6VGLM-4.6V: Text, Images, Video · Mercury 2: Text
- Self-hostingGLM-4.6VPublishes downloadable weights
| Measure | Weight | GLM-4.6V | Mercury 2 |
|---|---|---|---|
| Price | 50% | 66 | 70 |
| Inputs & features | 30% | 70 | 45 |
| Context window | 20% | 24 | 24 |
| Overall | 100% | 59/100 | 53/100 |
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 |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | — | — |
| ECI rank | — | — |
| Price per million tokens | ||
| Input | $0.30 | $0.25 (best) |
| Output | $0.90 | $0.75 (best) |
| Cached input | — | $0.025 |
| Blended (3:1) | $0.45 | $0.375 (best) |
| Long-context rate | Same rate | Same rate |
| Price source | Official Z.AI API | Official Inception API |
| Limits | ||
| Context window | 128,000 tokens | 128,000 tokens |
| Max output | 32,768 tokens | 50,000 tokens (best) |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | Yes | No |
| PDFs | No | No |
| Audio | No | No |
| Video | Yes | No |
| Reasoning | Yes | Yeslow · medium · high |
| Tool calling | Yes | Yes |
| Structured output | No | Yes |
| Availability | ||
| Weights | Open | Proprietary |
| API model ID | glm-4.6v | mercury-2 |
| API providers | 10 (best) | 1 |
| Released | Dec 8, 2025 | Feb 24, 2026 |
| Knowledge cutoff | Apr 2025 | Jan 1, 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
Which should you choose?
Which is better: GLM-4.6V or Mercury 2?
GLM-4.6V is the better all-round choice, scoring 59/100 against Mercury 2 (53). It leads on inputs & features. Mercury 2 wins on price. 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 or Mercury 2?
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). 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).
Which scores higher on benchmarks?
There is no independent benchmark that covers both models yet. GLM-4.6V has not been scored yet and Mercury 2 has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-4.6V and Mercury 2 yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Both support tool calling for agent workflows.
Which has the bigger context window?
GLM-4.6V and Mercury 2 share the same 128,000-token context window. Maximum output per response: GLM-4.6V up to 32,768, Mercury 2 up to 50,000 tokens.
Which can read images, PDFs, audio or video?
GLM-4.6V accepts text, images and video; Mercury 2 accepts text. GLM-4.6V handles the widest range of inputs.
Are any of these open source?
GLM-4.6V publishes its weights and can be self-hosted; Mercury 2 is proprietary.
Which is newer?
Mercury 2 is the newest, released Feb 24, 2026. GLM-4.6V came out Dec 8, 2025. Knowledge cutoff: GLM-4.6V Apr 2025, Mercury 2 Jan 1, 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.
