Granite 4.2 8B vs Mercury 2.5
Mercury 2.5 leads Granite 4.2 8B on the Artificial Analysis Intelligence Index, 12.3 to 11.1, and Mercury 2.5 costs 1.3x less per output token ($0.15 vs $0.20 per million). Updated October 22, 2026; 4 benchmarks have a result for both.
Granite 4.2 8B or Mercury 2.5: which to pick
Only Mercury 2.5 has a coding score, 1.6 on the Vibe Coding Index; Granite 4.2 8B has none, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
- Shared results
- 4
- Granite 4.2 8B only
- 23
- Mercury 2.5 only
- 18
- Like-for-like categories
- 4 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
Mercury 2.5
Mercury 2.5 leads, 12.3 against 11.1.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Granite 4.2 8B has a AA Agentic Index result, 1.3.
Directional only
Long documents
Listed context window
Mercury 2.5
Mercury 2.5 takes 260K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mercury 2.5
Granite 4.2 8B $0.0002, Mercury 2.5 $0.0001 per turn.
List prices
At a glance
- Coding index
- Granite 4.2 8B not benchmarked, Mercury 2.5 1.6 from 2 benchmarks.
- Widest category
- General, 26.7 points apart on average over 1 shared benchmark.
- Widest single gap
- Long context reasoning (AA-LCR): Granite 4.2 8B 45.0% against Mercury 2.5 71.7%, 26.7 points apart on a 0 to 100 scale.
- Output price
- Granite 4.2 8B $0.20 against Mercury 2.5 $0.15 per million output tokens, 1.3x apart.
- Context window
- Granite 4.2 8B takes 131K tokens, Mercury 2.5 260K.
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 | Granite 4.2 8B | Bars | Mercury 2.5 | Ties |
|---|---|---|---|---|
| Coding | 0 | 1 | ||
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 1 | ||
| General | 0 | 1 | ||
| All shared boards | 0 | 4 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
| Benchmark | Granite 4.2 8B | Mercury 2.5 | Leader |
|---|---|---|---|
| AA Coding Index | 22.4 | Not measured | Only Granite 4.2 8B measured |
| Terminal-Bench 4 (Vals AI) | Not measured | 0.0% | Only Mercury 2.5 measured |
| Vibe Code Bench (Vals AI) | Not measured | 3.7% | Only Mercury 2.5 measured |
| Code migration (Vals AI) | Not measured | 4.5% | Only Mercury 2.5 measured |
| IOI (Vals AI) | Not measured | 2.4% | Only Mercury 2.5 measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 34.5% | Only Mercury 2.5 measured |
| SkillsBench (Vals AI) | Not measured | 18.1% | Only Mercury 2.5 measured |
| CyberBench (Vals AI) | Not measured | 45.5% | Only Mercury 2.5 measured |
| SciCode | 31.5% | 38.5% | Mercury 2.5 |
| ALE-Bench | Not measured | 301.6 | Only Mercury 2.5 measured |
| LMArena Text: coding | 1378 | Not measured | Only Granite 4.2 8B measured |
| LMArena Text: software and IT | 1367 | Not measured | Only Granite 4.2 8B measured |
| Neither model has a result on the other 31 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Granite 4.2 8B | Mercury 2.5 | Leader |
|---|---|---|---|
| AA Intelligence Index | 11.1 | 12.3 | Mercury 2.5 |
| 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 | Granite 4.2 8B | Mercury 2.5 | Leader |
|---|---|---|---|
| AA Agentic Index | 1.3 | Not measured | Only Granite 4.2 8B measured |
| Finance Agent (Vals AI) | Not measured | 18.9% | Only Mercury 2.5 measured |
| Tax Agent Bench (Vals AI) | Not measured | 2.5% | Only Mercury 2.5 measured |
| Legal Research Bench (Vals AI) | Not measured | 4.3% | Only Mercury 2.5 measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 8.8% | Only Mercury 2.5 measured |
| Terminal-Bench-Science (Vals AI) | Not measured | 0.0% | Only Mercury 2.5 measured |
| Neither model has a result on the other 36 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Granite 4.2 8B | Mercury 2.5 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 63.1% | Not measured | Only Granite 4.2 8B measured |
| Humanity's Last Exam (Artificial Analysis run) | 9.7% | 11.8% | Mercury 2.5 |
| LMArena Text: hard prompts | 1327 | Not measured | Only Granite 4.2 8B measured |
| LMArena Text: expert | 1386 | Not measured | Only Granite 4.2 8B measured |
| LMArena Text: hard prompts in English | 1345 | Not measured | Only Granite 4.2 8B measured |
| Neither model has a result on the other 18 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Granite 4.2 8B | Mercury 2.5 | Leader |
|---|---|---|---|
| ProofBench | Not measured | 3.0% | Only Mercury 2.5 measured |
| Neither model has a result on the other 13 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Granite 4.2 8B | Mercury 2.5 | Leader |
|---|---|---|---|
| LegalBench (Vals AI) | Not measured | 83.1% | Only Mercury 2.5 measured |
| MedCode (Vals AI) | Not measured | 31.3% | Only Mercury 2.5 measured |
| LMArena Text: science | 1336 | Not measured | Only Granite 4.2 8B measured |
| LMArena Text: medicine and healthcare | 1336 | Not measured | Only Granite 4.2 8B measured |
| LMArena Text: legal and government | 1327 | Not measured | Only Granite 4.2 8B measured |
| LMArena Text: business and finance | 1322 | Not measured | Only Granite 4.2 8B measured |
| Neither model has a result on the other 14 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Granite 4.2 8B | Mercury 2.5 | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Granite 4.2 8B | Mercury 2.5 | Leader |
|---|---|---|---|
| Public Benefits Bench (Vals AI) | Not measured | 45.3% | Only Mercury 2.5 measured |
| MedScribe (Vals AI) | Not measured | 55.1% | Only Mercury 2.5 measured |
| Long context reasoning (AA-LCR) | 45.0% | 71.7% | Mercury 2.5 |
| LMArena Text | 1319 | Not measured | Only Granite 4.2 8B measured |
| LMArena Text: instruction following | 1300 | Not measured | Only Granite 4.2 8B measured |
| LMArena Text: creative writing | 1236 | Not measured | Only Granite 4.2 8B measured |
| LMArena Text: English | 1337 | Not measured | Only Granite 4.2 8B measured |
| LMArena Text: non-English | 1301 | Not measured | Only Granite 4.2 8B measured |
| LMArena Text: Chinese | 1367 | Not measured | Only Granite 4.2 8B measured |
| LMArena Text: Russian | 1282 | Not measured | Only Granite 4.2 8B measured |
| LMArena Text: multi-turn | 1301 | Not measured | Only Granite 4.2 8B measured |
| LMArena Text: longer queries | 1325 | Not measured | Only Granite 4.2 8B measured |
| LMArena Text: writing and language | 1269 | Not measured | Only Granite 4.2 8B measured |
| LMArena Text: entertainment and media | 1271 | Not measured | Only Granite 4.2 8B measured |
| Neither model has a result on the other 34 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Granite 4.2 8B | Mercury 2.5 | 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 | IBM Granite 4.2 8B | Inception Mercury 2.5 |
|---|---|---|
| Maker | IBM Granite | Inception |
| Weights | Open weights | No public weights listed |
| Released | August 31, 2026 | September 8, 2026 |
| Context window | 131K | 260K |
| Input $/Mtok | $0.08 | $0.04 |
| Output $/Mtok | $0.20 | $0.15 |
| Coding index | Not benchmarked | 1.6 |
| Independent measurements behind it | 0 | 2 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Granite 4.2 8B better than Mercury 2.5 for coding?
Only Mercury 2.5 has a coding score, 1.6 on the Vibe Coding Index; Granite 4.2 8B has none, so the two cannot be ranked on coding. Compare them on price, context and the boards below.
Which is cheaper, Granite 4.2 8B or Mercury 2.5?
Granite 4.2 8B costs $0.08 per million input tokens and $0.20 per million output tokens; Mercury 2.5 costs $0.04 and $0.15. Cost per solved task is known for neither of them.
Which has the bigger context window, Granite 4.2 8B or Mercury 2.5?
Mercury 2.5, with 260K tokens against 131K.
How many benchmarks is the Granite 4.2 8B vs Mercury 2.5 comparison based on?
4 benchmarks have a result for both models as of October 22, 2026. Granite 4.2 8B's coding index rests on 0 independent benchmarks and Mercury 2.5's on 2. Where only one model has a result, the page says so instead of naming a winner.