Mercury 2 vs GLM-4.6V vs Nemotron 3 Super 120B A12B
GLM-4.6V comes out ahead, 59 to 54 and 53 on our weighted score, though Nemotron 3 Super 120B A12B is 22% cheaper per token.
InceptionMercury 2
53/100- ECI—
- Price$0.25 / $0.75
- Context128K
- Our pick
Z.ai (Zhipu)GLM-4.6V
59/100- ECI—
- Price$0.30 / $0.90
- Context128K
NVIDIANemotron 3 Super 120B A12B
54/100- ECI—
- Price$0.20 / $0.80
- Context262K
GLM-4.6V is our pick
GLM-4.6V is the better all-round choice, scoring 59/100 against Nemotron 3 Super 120B A12B (54) and Mercury 2 (53). It leads on inputs & features. Nemotron 3 Super 120B A12B 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 priceNemotron 3 Super 120B A12BNemotron 3 Super 120B A12B $0.35 · Mercury 2 $0.375 · GLM-4.6V $0.45 per 1M tokens (3:1 blend)
- Longest contextNemotron 3 Super 120B A12BNemotron 3 Super 120B A12B 262,144 · Mercury 2 128,000 · GLM-4.6V 128,000 tokens
- Widest inputsGLM-4.6VMercury 2: Text · GLM-4.6V: Text, Images, Video · Nemotron 3 Super 120B A12B: Text
- Self-hostingGLM-4.6V and Nemotron 3 Super 120B A12BPublishes downloadable weights
| Measure | Weight | Mercury 2 | GLM-4.6V | Nemotron 3 Super 120B A12B |
|---|---|---|---|---|
| Price | 50% | 70 | 66 | 72 |
| Inputs & features | 30% | 45 | 70 | 35 |
| Context window | 20% | 24 | 24 | 37 |
| Overall | 100% | 53/100 | 59/100 | 54/100 |
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 | Mercury 2Inception | GLM-4.6VZ.ai (Zhipu) | Nemotron 3 Super 120B A12BNVIDIA |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.25 | $0.30 | $0.20 (best) |
| Output | $0.75 (best) | $0.90 | $0.80 |
| Cached input | $0.025 | — | — |
| Blended (3:1) | $0.375 | $0.45 | $0.35 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Inception API | Official Z.AI API | Official Nvidia API |
| Limits | |||
| Context window | 128,000 tokens | 128,000 tokens | 262,144 tokens (best) |
| Max output | 50,000 tokens | 32,768 tokens | 262,144 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | Yes | No |
| Reasoning | Yeslow · medium · high | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | No |
| Availability | |||
| Weights | Proprietary | Open | Open |
| API model ID | mercury-2 | glm-4.6v | nvidia/nemotron-3-super-120b-a12b |
| API providers | 1 | 10 | 19 (best) |
| Released | Feb 24, 2026 | Dec 8, 2025 | Mar 11, 2026 |
| Knowledge cutoff | Jan 1, 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.
- Mercury 2$4.00
- GLM-4.6V$4.80
- Nemotron 3 Super 120B A12B$3.60
Which should you choose?
Which is better: Mercury 2, GLM-4.6V or Nemotron 3 Super 120B A12B?
GLM-4.6V is the better all-round choice, scoring 59/100 against Nemotron 3 Super 120B A12B (54) and Mercury 2 (53). It leads on inputs & features. Nemotron 3 Super 120B A12B 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, Mercury 2, GLM-4.6V or Nemotron 3 Super 120B A12B?
Nemotron 3 Super 120B A12B is cheaper at $0.20 input / $0.80 output per million tokens (official Nvidia API price). Mercury 2 costs $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 $0.35 per million tokens for Nemotron 3 Super 120B A12B versus $0.375 for Mercury 2 (1.1× as much) and $0.45 for GLM-4.6V (1.3× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Mercury 2 has not been scored yet, GLM-4.6V has not been scored yet and Nemotron 3 Super 120B A12B has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Mercury 2, GLM-4.6V and Nemotron 3 Super 120B A12B 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?
Nemotron 3 Super 120B A12B has the largest context window at 262,144 tokens, against 128,000 for Mercury 2 and 128,000 for GLM-4.6V. Maximum output per response: Mercury 2 up to 50,000, GLM-4.6V up to 32,768, Nemotron 3 Super 120B A12B up to 262,144 tokens.
Which can read images, PDFs, audio or video?
Mercury 2 accepts text; GLM-4.6V accepts text, images and video; Nemotron 3 Super 120B A12B accepts text. GLM-4.6V handles the widest range of inputs.
Are any of these open source?
GLM-4.6V and Nemotron 3 Super 120B A12B publishes its weights and can be self-hosted; Mercury 2 is proprietary.
Which is newer?
Nemotron 3 Super 120B A12B is the newest, released Mar 11, 2026. Mercury 2 came out Feb 24, 2026; GLM-4.6V came out Dec 8, 2025. Knowledge cutoff: Mercury 2 Jan 1, 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.
