Mercury 3 vs Pareto
Mercury 3 comes out ahead, 67 to 33 on our weighted score, and it is the cheaper option too.
Mercury 3 is our pick
Mercury 3 is the better all-round choice, scoring 67/100 against Pareto (33). It leads on price and context window. Pareto 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 priceMercury 3Mercury 3 $0.163 · Pareto $3.86 per 1M tokens (3:1 blend)
- Longest contextMercury 3Mercury 3 1,000,000 · Pareto 262,144 tokens
- Widest inputsParetoMercury 3: Text · Pareto: Text, Images
- Self-hostingNo open weightsBoth are available only through APIs
| Measure | Weight | Mercury 3 | Pareto |
|---|---|---|---|
| Price | 50% | 84 | 21 |
| Inputs & features | 30% | 45 | 50 |
| Context window | 20% | 60 | 37 |
| Overall | 100% | 67/100 | 33/100 |
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 3Inception | ParetoUnbiased |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | — | — |
| ECI rank | — | — |
| Price per million tokens | ||
| Input | $0.10 (best) | $2.57 |
| Output | $0.35 (best) | $7.72 |
| Cached input | $0.01 | — |
| Blended (3:1) | $0.163 (best) | $3.86 |
| Long-context rate | Same rate | Same rate |
| Price source | Official Inception API | Median of 2 providers |
| Limits | ||
| Context window | 1,000,000 tokens (best) | 262,144 tokens |
| Max output | 131,072 tokens | 131,072 tokens |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | No | Yes |
| PDFs | No | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | Yeslow · medium · high | No |
| Tool calling | Yes | Yes |
| Structured output | Yes | No |
| Availability | ||
| Weights | Proprietary | Proprietary |
| API model ID | mercury-3 | — |
| API providers | 3 (best) | 2 |
| Released | Oct 16, 2026 | Sep 17, 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.
- Mercury 3$1.70
- Pareto$41.16
Which should you choose?
Which is better: Mercury 3 or Pareto?
Mercury 3 is the better all-round choice, scoring 67/100 against Pareto (33). It leads on price and context window. Pareto 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, Mercury 3 or Pareto?
Mercury 3 is cheaper at $0.10 input / $0.35 output per million tokens (official Inception API price). Pareto costs $2.57 input / $7.72 output per million tokens (median across 2 API providers). At a typical mix of three input tokens to one output token, that is $0.163 per million tokens for Mercury 3 versus $3.86 for Pareto (24× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers both models yet. Mercury 3 has not been scored yet and Pareto has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Mercury 3 and Pareto 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?
Mercury 3 has the largest context window at 1,000,000 tokens, against 262,144 for Pareto. Maximum output per response: Mercury 3 up to 131,072, Pareto up to 131,072 tokens.
Which can read images, PDFs, audio or video?
Mercury 3 accepts text; Pareto accepts text and images. Pareto handles the widest range of inputs.
Are any of these open source?
No. Mercury 3 and Pareto are proprietary and only available through APIs and apps.
Which is newer?
Mercury 3 is the newest, released Oct 16, 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.
