Mercury 2.5 vs Ling 3.1 Flash
Ling 3.1 Flash leads Mercury 2.5 on the Artificial Analysis Intelligence Index, 41.1 to 12.3, and Mercury 2.5 costs 1.5x less per output token ($0.15 vs $0.225 per million). Updated October 22, 2026; 4 benchmarks have a result for both.
Mercury 2.5 or Ling 3.1 Flash: which to pick
Only Mercury 2.5 has a coding score, 1.6 on the Vibe Coding Index; Ling 3.1 Flash has none, so the two cannot be ranked on coding.
Pick Ling 3.1 Flash for a larger context window (262K against 260K tokens) and work measured by SciCode or AA Intelligence Index, where it leads.
- Shared results
- 4
- Mercury 2.5 only
- 18
- Ling 3.1 Flash only
- 0
- 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
Ling 3.1 Flash
Ling 3.1 Flash leads, 41.1 against 12.3.
Same board, both measured
Long documents
Listed context window
Ling 3.1 Flash
Ling 3.1 Flash takes 262K tokens against 260K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mercury 2.5
Mercury 2.5 $0.0001, Ling 3.1 Flash $0.0002 per turn.
List prices
At a glance
- Coding index
- Mercury 2.5 1.6 from 2 benchmarks, Ling 3.1 Flash not benchmarked.
- Widest category
- Intelligence, 28.8 points apart on average over 1 shared benchmark.
- Widest single gap
- AA Intelligence Index: Mercury 2.5 12.3 against Ling 3.1 Flash 41.1, 28.8 points apart on a 0 to 100 scale.
- Output price
- Mercury 2.5 $0.15 against Ling 3.1 Flash $0.225 per million output tokens, 1.5x apart.
- Context window
- Mercury 2.5 takes 260K tokens, Ling 3.1 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 3.1 Flash | 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 | Mercury 2.5 | Ling 3.1 Flash | 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% | 54.1% | Ling 3.1 Flash |
| ALE-Bench | 301.6 | Not measured | Only Mercury 2.5 measured |
| Neither model has a result on the other 34 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Mercury 2.5 | Ling 3.1 Flash | Leader |
|---|---|---|---|
| AA Intelligence Index | 12.3 | 41.1 | Ling 3.1 Flash |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | Mercury 2.5 | Ling 3.1 Flash | 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 |
| Neither model has a result on the other 37 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Mercury 2.5 | Ling 3.1 Flash | Leader |
|---|---|---|---|
| Humanity's Last Exam (Artificial Analysis run) | 11.8% | 39.4% | Ling 3.1 Flash |
| Neither model has a result on the other 22 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Mercury 2.5 | Ling 3.1 Flash | Leader |
|---|---|---|---|
| ProofBench | 3.0% | Not measured | 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 index2 boards, none shared
| Benchmark | Mercury 2.5 | Ling 3.1 Flash | 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 18 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mercury 2.5 | Ling 3.1 Flash | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Mercury 2.5 | Ling 3.1 Flash | 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% | 83.0% | Ling 3.1 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 3.1 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
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 | inclusionAI Ling 3.1 Flash |
|---|---|---|
| Maker | Inception | inclusionAI |
| Weights | No public weights listed | No public weights listed |
| Released | September 8, 2026 | October 2, 2026 |
| Context window | 260K | 262K |
| Input $/Mtok | $0.04 | $0.075 |
| Output $/Mtok | $0.15 | $0.225 |
| 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 Ling 3.1 Flash for coding?
Only Mercury 2.5 has a coding score, 1.6 on the Vibe Coding Index; Ling 3.1 Flash has none, so the two cannot be ranked on coding. Pick Ling 3.1 Flash for a larger context window (262K against 260K tokens) and work measured by SciCode or AA Intelligence Index, where it leads.
Which is cheaper, Mercury 2.5 or Ling 3.1 Flash?
Mercury 2.5 costs $0.04 per million input tokens and $0.15 per million output tokens; Ling 3.1 Flash costs $0.075 and $0.225. Cost per solved task is known for neither of them.
Which has the bigger context window, Mercury 2.5 or Ling 3.1 Flash?
Ling 3.1 Flash, with 262K tokens against 260K.
How many benchmarks is the Mercury 2.5 vs Ling 3.1 Flash comparison based on?
4 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 3.1 Flash's on 0. Where only one model has a result, the page says so instead of naming a winner.