Mercury 2.5 vs Ling 3.0 Flash
Ling 3.0 Flash leads Mercury 2.5 on coding, 29.6 to 1.6 on the Vibe Coding Index, and Ling 3.0 Flash costs 1.2x less per output token ($0.1215 vs $0.15 per million). Updated October 22, 2026; 15 benchmarks have a result for both.
Mercury 2.5 or Ling 3.0 Flash: which to pick
Pick Ling 3.0 Flash for coding: it scores 29.6 on the Vibe Coding Index against 1.6 for Mercury 2.5.
Pick Mercury 2.5 for work measured by Vibe Code Bench (Vals AI) or Code migration (Vals AI), where it leads.
- Shared results
- 15
- Mercury 2.5 only
- 7
- Ling 3.0 Flash only
- 9
- Like-for-like categories
- 6 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
Ling 3.0 Flash
Ling 3.0 Flash scores 29.6 against 1.6.
2 benchmarks behind each score
Intelligence
AA Intelligence Index
Ling 3.0 Flash
Ling 3.0 Flash leads, 20.1 against 12.3.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Ling 3.0 Flash has a AA Agentic Index result, 19.3.
Directional only
Reasoning
GPQA Diamond
No call
Only Ling 3.0 Flash has a GPQA Diamond result, 84.8%.
Directional only
Knowledge
MMLU Pro
No call
Only Ling 3.0 Flash has a MMLU Pro result, 82.0%.
Directional only
Long documents
Listed context window
Ling 3.0 Flash
Ling 3.0 Flash takes 262K tokens against 260K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Ling 3.0 Flash
Mercury 2.5 $0.0001, Ling 3.0 Flash $0.0001 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Ling 3.0 Flash has the number, $0.05.
Directional only
At a glance
- Coding index
- Mercury 2.5 1.6 from 2 benchmarks, Ling 3.0 Flash 29.6 from 2 benchmarks.
- Widest category
- Reasoning, 11.9 points apart on average over 1 shared benchmark.
- Widest single gap
- MedScribe (Vals AI): Mercury 2.5 55.1% against Ling 3.0 Flash 80.9%, 25.8 points apart on a 0 to 100 scale.
- Output price
- Mercury 2.5 $0.15 against Ling 3.0 Flash $0.1215 per million output tokens, 1.2x apart.
- Context window
- Mercury 2.5 takes 260K tokens, Ling 3.0 Flash 262K.
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 | Ling 3.0 Flash | Ties |
|---|---|---|---|---|
| Coding | 2 | 3 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 1 | 2 | ||
| Reasoning | 0 | 1 | ||
| Knowledge | 1 | 1 | ||
| General | 0 | 3 | ||
| All shared boards | 4 | 11 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index23
| Benchmark | Mercury 2.5 | Ling 3.0 Flash | Leader |
|---|---|---|---|
| AA Coding Index | Not measured | 50.6 | Only Ling 3.0 Flash measured |
| LiveCodeBench (Vals AI) | Not measured | 84.0% | Only Ling 3.0 Flash measured |
| SWE-bench Verified (Vals AI) | Not measured | 65.2% | Only Ling 3.0 Flash measured |
| Terminal-Bench 4 (Vals AI) | 0.0% | Not measured | Only Mercury 2.5 measured |
| Vibe Code Bench (Vals AI) | 3.7% | 2.9% | Mercury 2.5 |
| Code migration (Vals AI) | 4.5% | 0.0% | Mercury 2.5 |
| IOI (Vals AI) | 2.4% | Not measured | Only Mercury 2.5 measured |
| Terminal-Bench 2.1 (Vals AI) | 34.5% | 50.2% | Ling 3.0 Flash |
| SkillsBench (Vals AI) | 18.1% | 27.9% | Ling 3.0 Flash |
| CyberBench (Vals AI) | 45.5% | Not measured | Only Mercury 2.5 measured |
| SciCode | 38.5% | 42.0% | Ling 3.0 Flash |
| ALE-Bench | 301.6 | Not measured | Only Mercury 2.5 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 | Mercury 2.5 | Ling 3.0 Flash | Leader |
|---|---|---|---|
| AA Intelligence Index | 12.3 | 20.1 | Ling 3.0 Flash |
| Neither model has a result on the other 1 board here | |||
Agentic workLike-for-likePublic boards, shown as published, kept out of the index12
| Benchmark | Mercury 2.5 | Ling 3.0 Flash | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 19.3 | Only Ling 3.0 Flash measured |
| Finance Agent (Vals AI) | 18.9% | 30.3% | Ling 3.0 Flash |
| Tax Agent Bench (Vals AI) | 2.5% | Not measured | Only Mercury 2.5 measured |
| Legal Research Bench (Vals AI) | 4.3% | 0.0% | Mercury 2.5 |
| Excel Modeling Benchmark (Vals AI) | 8.8% | 25.0% | Ling 3.0 Flash |
| Terminal-Bench-Science (Vals AI) | 0.0% | Not measured | 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 | Mercury 2.5 | Ling 3.0 Flash | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 84.8% | Only Ling 3.0 Flash measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 85.5% | Only Ling 3.0 Flash measured |
| Humanity's Last Exam (Artificial Analysis run) | 11.8% | 23.7% | Ling 3.0 Flash |
| 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 | Ling 3.0 Flash | Leader |
|---|---|---|---|
| ProofBench | 3.0% | Not measured | Only Mercury 2.5 measured |
| Neither model has a result on the other 13 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | Mercury 2.5 | Ling 3.0 Flash | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 82.0% | Only Ling 3.0 Flash measured |
| LegalBench (Vals AI) | 83.1% | 79.7% | Mercury 2.5 |
| CorpFin (Vals AI) | Not measured | 61.9% | Only Ling 3.0 Flash measured |
| TaxEval (Vals AI) | Not measured | 70.7% | Only Ling 3.0 Flash measured |
| MedCode (Vals AI) | 31.3% | 32.3% | Ling 3.0 Flash |
| 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 | Ling 3.0 Flash | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | Mercury 2.5 | Ling 3.0 Flash | Leader |
|---|---|---|---|
| Public Benefits Bench (Vals AI) | 45.3% | 50.7% | Ling 3.0 Flash |
| MedScribe (Vals AI) | 55.1% | 80.9% | Ling 3.0 Flash |
| Long context reasoning (AA-LCR) | 71.7% | 73.0% | Ling 3.0 Flash |
| Neither model has a result on the other 45 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mercury 2.5 | Ling 3.0 Flash | 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
Ling 3.0 Flash costs less
Repository review
50K input + 3K output tokens
Ling 3.0 Flash costs less
Agent loop
200K input + 10K output tokens
Ling 3.0 Flash 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 | inclusionAI Ling 3.0 Flash |
|---|---|---|
| Maker | Inception | inclusionAI |
| Weights | No public weights listed | Open weights |
| Released | September 8, 2026 | July 23, 2026 |
| Context window | 260K | 262K |
| Input $/Mtok | $0.04 | $0.0405 |
| Output $/Mtok | $0.15 | $0.1215 |
| Coding index | 1.6 | 29.6 |
| Independent measurements behind it | 2 | 2 |
| Cost per solved task | Not computable | $0.05 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Mercury 2.5 better than Ling 3.0 Flash for coding?
Pick Ling 3.0 Flash for coding: it scores 29.6 on the Vibe Coding Index against 1.6 for Mercury 2.5. Pick Mercury 2.5 for work measured by Vibe Code Bench (Vals AI) or Code migration (Vals AI), where it leads.
Which is cheaper, Mercury 2.5 or Ling 3.0 Flash?
Mercury 2.5 costs $0.04 per million input tokens and $0.15 per million output tokens; Ling 3.0 Flash costs $0.0405 and $0.1215. Cost per solved task is known for Ling 3.0 Flash only.
Which has the bigger context window, Mercury 2.5 or Ling 3.0 Flash?
Ling 3.0 Flash, with 262K tokens against 260K.
How many benchmarks is the Mercury 2.5 vs Ling 3.0 Flash comparison based on?
15 benchmarks have a result for both models as of October 22, 2026. Mercury 2.5's coding index rests on 2 independent benchmarks and Ling 3.0 Flash's on 2. Where only one model has a result, the page says so instead of naming a winner.