Pareto vs Nemotron 3.5 Super 120B A12B vs Kimi K3.1 Pro
Nemotron 3.5 Super 120B A12B comes out ahead, 59 to 46 and 33 on our weighted score, and it is the cheaper option too.
Unbiased
Pareto
33/100- ECI—
- Price$2.57 / $7.72
- Context262K
- Our pick
NVIDIANemotron 3.5 Super 120B A12B
59/100- ECI—
- Price$0.20 / $0.50
- Context1.05M
Moonshot AIKimi K3.1 Pro
46/100- ECI—
- Price$2.00 / $10.00
- Context1.05M
Nemotron 3.5 Super 120B A12B is our pick
Nemotron 3.5 Super 120B A12B is the better all-round choice, scoring 59/100 against Kimi K3.1 Pro (46) and Pareto (33). It leads on price. Kimi K3.1 Pro wins 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 3.5 Super 120B A12BNemotron 3.5 Super 120B A12B $0.275 · Pareto $3.86 · Kimi K3.1 Pro $4.00 per 1M tokens (3:1 blend)
- Longest contextNemotron 3.5 Super 120B A12B and Kimi K3.1 ProNemotron 3.5 Super 120B A12B 1,048,576 · Kimi K3.1 Pro 1,048,576 · Pareto 262,144 tokens
- Widest inputsKimi K3.1 ProPareto: Text, Images · Nemotron 3.5 Super 120B A12B: Text · Kimi K3.1 Pro: Text, Images, Video
- Self-hostingNemotron 3.5 Super 120B A12BPublishes downloadable weights
| Measure | Weight | Pareto | Nemotron 3.5 Super 120B A12B | Kimi K3.1 Pro |
|---|---|---|---|---|
| Price | 50% | 21 | 73 | 20 |
| Inputs & features | 30% | 50 | 35 | 80 |
| Context window | 20% | 37 | 61 | 61 |
| Overall | 100% | 33/100 | 59/100 | 46/100 |
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 | ParetoUnbiased | Nemotron 3.5 Super 120B A12BNVIDIA | Kimi K3.1 ProMoonshot AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $2.57 | $0.20 (best) | $2.00 |
| Output | $7.72 | $0.50 (best) | $10.00 |
| Cached input | — | — | $0.20 |
| Blended (3:1) | $3.86 | $0.275 (best) | $4.00 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 2 providers | Median of 1 providers | Official Moonshot AI API |
| Limits | |||
| Context window | 262,144 tokens | 1,048,576 tokens (best) | 1,048,576 tokens (best) |
| Max output | 131,072 tokens | 131,072 tokens | 131,072 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | Yes |
| Reasoning | No | Yes | Yeslow · high · max |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | Proprietary | Open | Proprietary |
| API model ID | — | — | kimi-k3.1-pro |
| API providers | 2 | 1 | 17 (best) |
| Released | Sep 17, 2026 | Oct 15, 2026 | Oct 23, 2026 |
| Knowledge cutoff | — | — | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Pareto$41.16
- Nemotron 3.5 Super 120B A12B$3.00
- Kimi K3.1 Pro$40.00
Which should you choose?
Which is better: Pareto, Nemotron 3.5 Super 120B A12B or Kimi K3.1 Pro?
Nemotron 3.5 Super 120B A12B is the better all-round choice, scoring 59/100 against Kimi K3.1 Pro (46) and Pareto (33). It leads on price. Kimi K3.1 Pro wins 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, Pareto, Nemotron 3.5 Super 120B A12B or Kimi K3.1 Pro?
Nemotron 3.5 Super 120B A12B is cheaper at $0.20 input / $0.50 output per million tokens (median across 1 API provider). Pareto costs $2.57 input / $7.72 output per million tokens (median across 2 API providers); Kimi K3.1 Pro costs $2.00 input / $10.00 output per million tokens (official Moonshot AI API price). At a typical mix of three input tokens to one output token, that is $0.275 per million tokens for Nemotron 3.5 Super 120B A12B versus $3.86 for Pareto (14× as much) and $4.00 for Kimi K3.1 Pro (15× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Pareto has not been scored yet, Nemotron 3.5 Super 120B A12B has not been scored yet and Kimi K3.1 Pro has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Pareto, Nemotron 3.5 Super 120B A12B and Kimi K3.1 Pro 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 3.5 Super 120B A12B and Kimi K3.1 Pro have the largest context windows (1,048,576 and 1,048,576 tokens), against 262,144 for Pareto. Maximum output per response: Pareto up to 131,072, Nemotron 3.5 Super 120B A12B up to 131,072, Kimi K3.1 Pro up to 131,072 tokens.
Which can read images, PDFs, audio or video?
Pareto accepts text and images; Nemotron 3.5 Super 120B A12B accepts text; Kimi K3.1 Pro accepts text, images and video. Kimi K3.1 Pro handles the widest range of inputs.
Are any of these open source?
Nemotron 3.5 Super 120B A12B publishes its weights and can be self-hosted; Pareto and Kimi K3.1 Pro is proprietary.
Which is newer?
Kimi K3.1 Pro is the newest, released Oct 23, 2026. Nemotron 3.5 Super 120B A12B came out Oct 15, 2026; Pareto came out Sep 17, 2026.
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 Nov 2, 2026.
