Flint vs Mercury 3
Mercury 3 comes out ahead, 51 to 36 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 51/100 against Flint (36). It leads on inputs & features and context window. The score weighs inputs & features 60%, context window 40%. 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 per 1M tokens (3:1 blend) · Flint unpriced
- Longest contextMercury 3Mercury 3 1,000,000 · Flint 262,144 tokens
- Widest inputsSame inputsFlint: Text · Mercury 3: Text
- Self-hostingFlintPublishes downloadable weights (Apache-2.0)
| Measure | Weight | Flint | Mercury 3 |
|---|---|---|---|
| Inputs & features | 60% | 35 | 45 |
| Context window | 40% | 37 | 60 |
| Overall | 100% | 36/100 | 51/100 |
Left out because at least one model lacks the data: capability and price. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | FlintRecursive |  Mercury 3Inception |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | — | — |
| ECI rank | — | — |
| Price per million tokens | ||
| Input | — | $0.10 |
| Output | — | $0.35 |
| Cached input | — | $0.01 |
| Blended (3:1) | — | $0.163 |
| Long-context rate | — | Same rate |
| Price source | — | Official Inception API |
| Limits | ||
| Context window | 262,144 tokens | 1,000,000 tokens (best) |
| Max output | 262,144 tokens (best) | 131,072 tokens |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | No | No |
| PDFs | No | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | Yes | Yeslow · medium · high |
| Tool calling | Yes | Yes |
| Structured output | No | Yes |
| Availability | ||
| Weights | OpenApache-2.0 | Proprietary |
| API model ID | — | mercury-3 |
| API providers | 1 | 3 (best) |
| Released | Oct 21, 2026 | Oct 16, 2026 |
| Knowledge cutoff | Jun 2026 | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Flint—
- Mercury 3$1.70
Which should you choose?
Which is better: Flint or Mercury 3?
Mercury 3 is the better all-round choice, scoring 51/100 against Flint (36). It leads on inputs & features and context window. The score weighs inputs & features 60%, context window 40%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, Flint or Mercury 3?
Mercury 3 is cheaper at $0.10 input / $0.35 output per million tokens (official Inception API price). . At a typical mix of three input tokens to one output token, that is $0.163 per million tokens for Mercury 3 versus . Flint has no published per-token price.
Which scores higher on benchmarks?
There is no independent benchmark that covers both models yet. Flint has not been scored yet and Mercury 3 has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Flint and Mercury 3 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 Flint. Maximum output per response: Flint up to 262,144, Mercury 3 up to 131,072 tokens.
Which can read images, PDFs, audio or video?
Flint accepts text; Mercury 3 accepts text. They handle the same number of input types.
Are any of these open source?
Flint publishes its weights (Apache-2.0) and can be self-hosted; Mercury 3 is proprietary.
Which is newer?
Flint is the newest, released Oct 21, 2026. Mercury 3 came out Oct 16, 2026. Knowledge cutoff: Flint Jun 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.
