Mercury 2 vs Nemotron 3 Super 120B A12B vs Qwen3.5 122B-A10B
Qwen3.5 122B-A10B comes out ahead, 58 to 54 and 53 on our weighted score, though Nemotron 3 Super 120B A12B is 3.1× cheaper per token.
InceptionMercury 2
53/100- ECI—
- Price$0.25 / $0.75
- Context128K
NVIDIANemotron 3 Super 120B A12B
54/100- ECI—
- Price$0.20 / $0.80
- Context262K
- Our pick
Alibaba (Qwen)Qwen3.5 122B-A10B
58/100- ECI—
- Price$0.40 / $3.20
- Context262K
Qwen3.5 122B-A10B is our pick
Qwen3.5 122B-A10B is the better all-round choice, scoring 58/100 against Nemotron 3 Super 120B A12B (54) and Mercury 2 (53). It leads on inputs & features. 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 · Qwen3.5 122B-A10B $1.10 per 1M tokens (3:1 blend)
- Longest contextNemotron 3 Super 120B A12B and Qwen3.5 122B-A10BNemotron 3 Super 120B A12B 262,144 · Qwen3.5 122B-A10B 262,144 · Mercury 2 128,000 tokens
- Widest inputsQwen3.5 122B-A10BMercury 2: Text · Nemotron 3 Super 120B A12B: Text · Qwen3.5 122B-A10B: Text, Images, Audio, Video
- Self-hostingNemotron 3 Super 120B A12B and Qwen3.5 122B-A10BPublishes downloadable weights
| Measure | Weight | Mercury 2 | Nemotron 3 Super 120B A12B | Qwen3.5 122B-A10B |
|---|---|---|---|---|
| Price | 50% | 70 | 72 | 48 |
| Inputs & features | 30% | 45 | 35 | 90 |
| Context window | 20% | 24 | 37 | 37 |
| Overall | 100% | 53/100 | 54/100 | 58/100 |
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 | Nemotron 3 Super 120B A12BNVIDIA | Qwen3.5 122B-A10BAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.25 | $0.20 (best) | $0.40 |
| Output | $0.75 (best) | $0.80 | $3.20 |
| Cached input | $0.025 | — | — |
| Blended (3:1) | $0.375 | $0.35 (best) | $1.10 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Inception API | Official Nvidia API | Official Alibaba API |
| Limits | |||
| Context window | 128,000 tokens | 262,144 tokens (best) | 262,144 tokens (best) |
| Max output | 50,000 tokens | 262,144 tokens (best) | 65,536 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | Yes |
| PDFs | No | No | No |
| Audio | No | No | Yes |
| Video | No | No | Yes |
| Reasoning | Yeslow · medium · high | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | Yes |
| Availability | |||
| Weights | Proprietary | Open | Open |
| API model ID | mercury-2 | nvidia/nemotron-3-super-120b-a12b | qwen3.5-122b-a10b |
| API providers | 1 | 19 (best) | 19 (best) |
| Released | Feb 24, 2026 | Mar 11, 2026 | Feb 23, 2026 |
| Knowledge cutoff | 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.
- Mercury 2$4.00
- Nemotron 3 Super 120B A12B$3.60
- Qwen3.5 122B-A10B$10.40
Which should you choose?
Which is better: Mercury 2, Nemotron 3 Super 120B A12B or Qwen3.5 122B-A10B?
Qwen3.5 122B-A10B is the better all-round choice, scoring 58/100 against Nemotron 3 Super 120B A12B (54) and Mercury 2 (53). It leads on inputs & features. 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, Nemotron 3 Super 120B A12B or Qwen3.5 122B-A10B?
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); Qwen3.5 122B-A10B costs $0.40 input / $3.20 output per million tokens (official Alibaba 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 $1.10 for Qwen3.5 122B-A10B (3.1× 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, Nemotron 3 Super 120B A12B has not been scored yet and Qwen3.5 122B-A10B has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Mercury 2, Nemotron 3 Super 120B A12B and Qwen3.5 122B-A10B 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 and Qwen3.5 122B-A10B have the largest context windows (262,144 and 262,144 tokens), against 128,000 for Mercury 2. Maximum output per response: Mercury 2 up to 50,000, Nemotron 3 Super 120B A12B up to 262,144, Qwen3.5 122B-A10B up to 65,536 tokens.
Which can read images, PDFs, audio or video?
Mercury 2 accepts text; Nemotron 3 Super 120B A12B accepts text; Qwen3.5 122B-A10B accepts text, images, audio and video. Qwen3.5 122B-A10B handles the widest range of inputs.
Are any of these open source?
Nemotron 3 Super 120B A12B and Qwen3.5 122B-A10B 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; Qwen3.5 122B-A10B came out Feb 23, 2026. Knowledge cutoff: 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.
