Mercury 2.5 vs o4 Mini High
Mercury 2.5 costs 29.3x less per output token than o4 Mini High ($0.15 vs $4.40 per million). Updated October 21, 2026; 1 benchmarks have a result for both.
Mercury 2.5 or o4 Mini High: which to pick
Only Mercury 2.5 has a coding score, 1.6 on the Vibe Coding Index; o4 Mini High has none, so the two cannot be ranked on coding.
Pick o4 Mini High for work measured by ALE-Bench, where it leads.
- Shared results
- 1
- Mercury 2.5 only
- 21
- o4 Mini High only
- 1
- Like-for-like categories
- 1 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Only Mercury 2.5 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
No call
Only Mercury 2.5 has a AA Intelligence Index result, 12.3.
Directional only
Long documents
Listed context window
Mercury 2.5
Mercury 2.5 takes 260K tokens against 200K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mercury 2.5
Mercury 2.5 $0.0001, o4 Mini High $0.0033 per turn.
List prices
At a glance
- Coding index
- Mercury 2.5 1.6 from 2 benchmarks, o4 Mini High not benchmarked.
- Widest category
- Coding, 17.5 points apart on average over 1 shared benchmark.
- Widest single gap
- ALE-Bench: Mercury 2.5 301.6 against o4 Mini High 826.2, 17.5 points apart on a 0 to 100 scale.
- Output price
- Mercury 2.5 $0.15 against o4 Mini High $4.40 per million output tokens, 29.3x apart.
- Context window
- Mercury 2.5 takes 260K tokens, o4 Mini High 200K.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | Mercury 2.5 | Bars | o4 Mini High | Ties |
|---|---|---|---|---|
| Coding | 0 | 1 | ||
| All shared boards | 0 | 1 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
| Benchmark | Mercury 2.5 | o4 Mini High | Leader |
|---|---|---|---|
| Terminal-Bench 4 (Vals AI) | 0.0% | Not measured | Only Mercury 2.5 measured |
| Vibe Code Bench (Vals AI) | 3.7% | Not measured | Only Mercury 2.5 measured |
| Code migration (Vals AI) | 4.5% | Not measured | Only Mercury 2.5 measured |
| IOI (Vals AI) | 2.4% | Not measured | Only Mercury 2.5 measured |
| Terminal-Bench 2.1 (Vals AI) | 34.5% | Not measured | Only Mercury 2.5 measured |
| SkillsBench (Vals AI) | 18.1% | Not measured | Only Mercury 2.5 measured |
| CyberBench (Vals AI) | 45.5% | Not measured | Only Mercury 2.5 measured |
| SciCode | 38.5% | Not measured | Only Mercury 2.5 measured |
| ALE-Bench | 301.6 | 826.2 | o4 Mini High |
| Neither model has a result on the other 29 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Mercury 2.5 | o4 Mini High | Leader |
|---|---|---|---|
| AA Intelligence Index | 12.3 | Not measured | Only Mercury 2.5 measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Mercury 2.5 | o4 Mini High | Leader |
|---|---|---|---|
| Finance Agent (Vals AI) | 18.9% | Not measured | Only Mercury 2.5 measured |
| Tax Agent Bench (Vals AI) | 2.5% | Not measured | Only Mercury 2.5 measured |
| Legal Research Bench (Vals AI) | 4.3% | Not measured | Only Mercury 2.5 measured |
| Excel Modeling Benchmark (Vals AI) | 8.8% | Not measured | Only Mercury 2.5 measured |
| Terminal-Bench-Science (Vals AI) | 0.0% | Not measured | Only Mercury 2.5 measured |
| GDPval | Not measured | 25.3% | Only o4 Mini High measured |
| Neither model has a result on the other 33 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Mercury 2.5 | o4 Mini High | Leader |
|---|---|---|---|
| Humanity's Last Exam (Artificial Analysis run) | 11.8% | Not measured | Only Mercury 2.5 measured |
| Neither model has a result on the other 20 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Mercury 2.5 | o4 Mini High | Leader |
|---|---|---|---|
| ProofBench | 3.0% | Not measured | Only Mercury 2.5 measured |
| Neither model has a result on the other 12 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Mercury 2.5 | o4 Mini High | Leader |
|---|---|---|---|
| LegalBench (Vals AI) | 83.1% | Not measured | Only Mercury 2.5 measured |
| MedCode (Vals AI) | 31.3% | Not measured | Only Mercury 2.5 measured |
| Neither model has a result on the other 15 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mercury 2.5 | o4 Mini High | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | Mercury 2.5 | o4 Mini High | Leader |
|---|---|---|---|
| Public Benefits Bench (Vals AI) | 45.3% | Not measured | Only Mercury 2.5 measured |
| MedScribe (Vals AI) | 55.1% | Not measured | Only Mercury 2.5 measured |
| Long context reasoning (AA-LCR) | 71.7% | Not measured | Only Mercury 2.5 measured |
| Neither model has a result on the other 43 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mercury 2.5 | o4 Mini High | Leader |
|---|---|---|---|
| Neither model has a result on 5 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
Mercury 2.5 costs less
Repository review
50K input + 3K output tokens
Mercury 2.5 costs less
Agent loop
200K input + 10K output tokens
Mercury 2.5 costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | Inception Mercury 2.5 | OpenAI o4 Mini High |
|---|---|---|
| Maker | Inception | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | September 8, 2026 | April 16, 2025 |
| Context window | 260K | 200K |
| Input $/Mtok | $0.04 | $1.10 |
| Output $/Mtok | $0.15 | $4.40 |
| Coding index | 1.6 | Not benchmarked |
| Independent measurements behind it | 2 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Mercury 2.5 better than o4 Mini High for coding?
Only Mercury 2.5 has a coding score, 1.6 on the Vibe Coding Index; o4 Mini High has none, so the two cannot be ranked on coding. Pick o4 Mini High for work measured by ALE-Bench, where it leads.
Which is cheaper, Mercury 2.5 or o4 Mini High?
Mercury 2.5 costs $0.04 per million input tokens and $0.15 per million output tokens; o4 Mini High costs $1.10 and $4.40. Cost per solved task is known for neither of them.
Which has the bigger context window, Mercury 2.5 or o4 Mini High?
Mercury 2.5, with 260K tokens against 200K.
How many benchmarks is the Mercury 2.5 vs o4 Mini High comparison based on?
1 benchmarks have a result for both models as of October 21, 2026. Mercury 2.5's coding index rests on 2 independent benchmarks and o4 Mini High's on 0. Where only one model has a result, the page says so instead of naming a winner.