Ling 3.0 Flash vs MiniMax M2.5
MiniMax M2.5 leads Ling 3.0 Flash on coding, 37.2 to 29.6 on the Vibe Coding Index, and Ling 3.0 Flash costs 9.9x less per output token ($0.1215 vs $1.20 per million). Updated October 23, 2026; 12 benchmarks have a result for both.
Ling 3.0 Flash or MiniMax M2.5: which to pick
Pick MiniMax M2.5 for coding: it scores 37.2 on the Vibe Coding Index against 29.6 for Ling 3.0 Flash.
Pick Ling 3.0 Flash for output tokens 9.9x cheaper ($0.1215 vs $1.20 per million) and a lower cost per solved task ($0.05 against $0.62).
- Shared results
- 12
- Ling 3.0 Flash only
- 12
- MiniMax M2.5 only
- 59
- Like-for-like categories
- 5 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
MiniMax M2.5
MiniMax M2.5 scores 37.2 against 29.6.
2 and 3 benchmarks behind the scores
Intelligence
AA Intelligence Index
MiniMax M2.5
MiniMax M2.5 leads, 22.8 against 20.1.
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
Ling 3.0 Flash
Ling 3.0 Flash leads, 84.8% against 82.1%.
Same board, both measured
Math
AIME
No call
Only MiniMax M2.5 has a AIME result, 88.8%.
Directional only
Knowledge
MMLU Pro
Ling 3.0 Flash
Ling 3.0 Flash leads, 82.0% against 80.1%.
Same board, both measured
Web design
Design Arena: websites
No call
Only MiniMax M2.5 has a Design Arena: websites result, 1222.
Directional only
Long documents
Listed context window
Ling 3.0 Flash
Ling 3.0 Flash takes 262K tokens against 205K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Ling 3.0 Flash
Ling 3.0 Flash $0.0001, MiniMax M2.5 $0.0009 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
Ling 3.0 Flash
Ling 3.0 Flash $0.05, MiniMax M2.5 $0.62.
Published attempt cost and pass rate
At a glance
- Coding index
- Ling 3.0 Flash 29.6 from 2 benchmarks, MiniMax M2.5 37.2 from 3 benchmarks.
- Widest category
- Coding, 8.6 points apart on average over 3 shared benchmarks.
- Widest single gap
- Vibe Code Bench (Vals AI): Ling 3.0 Flash 2.9% against MiniMax M2.5 14.8%, 11.9 points apart on a 0 to 100 scale.
- Output price
- Ling 3.0 Flash $0.1215 against MiniMax M2.5 $1.20 per million output tokens, 9.9x apart.
- Context window
- Ling 3.0 Flash takes 262K tokens, MiniMax M2.5 205K.
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 | Ling 3.0 Flash | Bars | MiniMax M2.5 | Ties |
|---|---|---|---|---|
| Coding | 1 | 2 | ||
| Intelligence | 0 | 1 | ||
| Reasoning | 3 | 0 | ||
| Knowledge | 3 | 1 | ||
| General | 0 | 1 | ||
| All shared boards | 7 | 5 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index12
| Benchmark | Ling 3.0 Flash | MiniMax M2.5 | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 75.8% | Only MiniMax M2.5 measured |
| SWE-bench Multilingual | Not measured | 68.3% | Only MiniMax M2.5 measured |
| LMArena WebDev | Not measured | 1386 | Only MiniMax M2.5 measured |
| AA Coding Index | 50.6 | Not measured | Only Ling 3.0 Flash measured |
| LiveCodeBench (Vals AI) | 84.0% | 79.2% | Ling 3.0 Flash |
| SWE-bench Verified (Vals AI) | 65.2% | 74.2% | MiniMax M2.5 |
| Vibe Code Bench (Vals AI) | 2.9% | 14.8% | MiniMax M2.5 |
| Code migration (Vals AI) | 0.0% | Not measured | Only Ling 3.0 Flash measured |
| Terminal-Bench 2.1 (Vals AI) | 50.2% | Not measured | Only Ling 3.0 Flash measured |
| SkillsBench (Vals AI) | 27.9% | Not measured | Only Ling 3.0 Flash measured |
| SciCode | 42.0% | Not measured | Only Ling 3.0 Flash measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 34.8% | Only MiniMax M2.5 measured |
| Terminal-Bench | Not measured | 42.7% | Only MiniMax M2.5 measured |
| ALE-Bench | Not measured | 618.2 | Only MiniMax M2.5 measured |
| LMArena Text: coding | Not measured | 1380 | Only MiniMax M2.5 measured |
| LMArena Text: software and IT | Not measured | 1383 | Only MiniMax M2.5 measured |
| LMArena WebDev: React | Not measured | 1374 | Only MiniMax M2.5 measured |
| LMArena WebDev: HTML | Not measured | 1414 | Only MiniMax M2.5 measured |
| SWE Atlas: Codebase QnA | Not measured | 10.3% | Only MiniMax M2.5 measured |
| SWE Atlas: Refactoring | Not measured | 19.5% | Only MiniMax M2.5 measured |
| SWE Atlas: Test Writing | Not measured | 18.6% | Only MiniMax M2.5 measured |
| Neither model has a result on the other 22 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Ling 3.0 Flash | MiniMax M2.5 | Leader |
|---|---|---|---|
| AA Intelligence Index | 20.1 | 22.8 | MiniMax M2.5 |
| Epoch Capabilities Index | Not measured | 146.6 | Only MiniMax M2.5 measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Ling 3.0 Flash | MiniMax M2.5 | Leader |
|---|---|---|---|
| AA Agentic Index | 19.3 | Not measured | Only Ling 3.0 Flash measured |
| Finance Agent (Vals AI) | 30.3% | Not measured | Only Ling 3.0 Flash measured |
| Legal Research Bench (Vals AI) | 0.0% | Not measured | Only Ling 3.0 Flash measured |
| Excel Modeling Benchmark (Vals AI) | 25.0% | Not measured | Only Ling 3.0 Flash measured |
| τ²-Bench Telecom | Not measured | 95.3% | Only MiniMax M2.5 measured |
| Vending-Bench 2 | Not measured | -$23 | Only MiniMax M2.5 measured |
| Neither model has a result on the other 36 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | Ling 3.0 Flash | MiniMax M2.5 | Leader |
|---|---|---|---|
| GPQA Diamond | 84.8% | 82.1% | Ling 3.0 Flash |
| GPQA Diamond (Artificial Analysis run) | 85.5% | 84.8% | Ling 3.0 Flash |
| Humanity's Last Exam (Artificial Analysis run) | 23.7% | 20.5% | Ling 3.0 Flash |
| ARC-AGI | Not measured | 63.7% | Only MiniMax M2.5 measured |
| ARC-AGI-2 | Not measured | 4.9% | Only MiniMax M2.5 measured |
| LMArena Text: hard prompts | Not measured | 1372 | Only MiniMax M2.5 measured |
| LMArena Text: expert | Not measured | 1379 | Only MiniMax M2.5 measured |
| LMArena Text: hard prompts in English | Not measured | 1384 | Only MiniMax M2.5 measured |
| Kagi LLM Benchmark | Not measured | 55.2% | Only MiniMax M2.5 measured |
| Neither model has a result on the other 14 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | Ling 3.0 Flash | MiniMax M2.5 | Leader |
|---|---|---|---|
| AIME | Not measured | 88.8% | Only MiniMax M2.5 measured |
| ProofBench | Not measured | 4.0% | Only MiniMax M2.5 measured |
| LMArena Text: math | Not measured | 1378 | Only MiniMax M2.5 measured |
| LMArena Text: mathematical work | Not measured | 1378 | Only MiniMax M2.5 measured |
| Neither model has a result on the other 10 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index31
| Benchmark | Ling 3.0 Flash | MiniMax M2.5 | Leader |
|---|---|---|---|
| MMLU Pro | 82.0% | 80.1% | Ling 3.0 Flash |
| LegalBench (Vals AI) | 79.7% | 80.0% | MiniMax M2.5 |
| CaseLaw (Vals AI) | Not measured | 53.5% | Only MiniMax M2.5 measured |
| CorpFin (Vals AI) | 61.9% | 59.6% | Ling 3.0 Flash |
| TaxEval (Vals AI) | 70.7% | 68.2% | Ling 3.0 Flash |
| MedQA (Vals AI) | Not measured | 92.5% | Only MiniMax M2.5 measured |
| MedCode (Vals AI) | 32.3% | Not measured | Only Ling 3.0 Flash measured |
| LMArena Text: science | Not measured | 1369 | Only MiniMax M2.5 measured |
| LMArena Text: medicine and healthcare | Not measured | 1357 | Only MiniMax M2.5 measured |
| LMArena Text: legal and government | Not measured | 1366 | Only MiniMax M2.5 measured |
| LMArena Text: business and finance | Not measured | 1356 | Only MiniMax M2.5 measured |
| Neither model has a result on the other 9 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | Ling 3.0 Flash | MiniMax M2.5 | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1222 | Only MiniMax M2.5 measured |
| Design Arena: UI components | Not measured | 1170 | Only MiniMax M2.5 measured |
| Design Arena: game dev | Not measured | 1176 | Only MiniMax M2.5 measured |
| Design Arena: data viz | Not measured | 1172 | Only MiniMax M2.5 measured |
| Design Arena: 3D | Not measured | 1170 | Only MiniMax M2.5 measured |
| Design Arena: all code categories | Not measured | 1207 | Only MiniMax M2.5 measured |
| Design Arena: SVG | Not measured | 1150 | Only MiniMax M2.5 measured |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Ling 3.0 Flash | MiniMax M2.5 | Leader |
|---|---|---|---|
| Public Benefits Bench (Vals AI) | 50.7% | Not measured | Only Ling 3.0 Flash measured |
| MedScribe (Vals AI) | 80.9% | Not measured | Only Ling 3.0 Flash measured |
| IFBench | Not measured | 71.6% | Only MiniMax M2.5 measured |
| Long context reasoning (AA-LCR) | 73.0% | 73.3% | MiniMax M2.5 |
| CL-bench | Not measured | 11.4% | Only MiniMax M2.5 measured |
| CL-bench Life | Not measured | 6.3% | Only MiniMax M2.5 measured |
| LMArena Text | Not measured | 1359 | Only MiniMax M2.5 measured |
| LMArena Text: instruction following | Not measured | 1353 | Only MiniMax M2.5 measured |
| LMArena Text: creative writing | Not measured | 1331 | Only MiniMax M2.5 measured |
| LMArena Text: English | Not measured | 1380 | Only MiniMax M2.5 measured |
| LMArena Text: non-English | Not measured | 1338 | Only MiniMax M2.5 measured |
| LMArena Text: Chinese | Not measured | 1393 | Only MiniMax M2.5 measured |
| LMArena Text: German | Not measured | 1361 | Only MiniMax M2.5 measured |
| LMArena Text: Japanese | Not measured | 1171 | Only MiniMax M2.5 measured |
| LMArena Text: Korean | Not measured | 1233 | Only MiniMax M2.5 measured |
| LMArena Text: French | Not measured | 1363 | Only MiniMax M2.5 measured |
| LMArena Text: Spanish | Not measured | 1353 | Only MiniMax M2.5 measured |
| LMArena Text: Russian | Not measured | 1359 | Only MiniMax M2.5 measured |
| LMArena Text: Polish | Not measured | 1334 | Only MiniMax M2.5 measured |
| LMArena Text: multi-turn | Not measured | 1364 | Only MiniMax M2.5 measured |
| LMArena Text: longer queries | Not measured | 1366 | Only MiniMax M2.5 measured |
| LMArena Text: writing and language | Not measured | 1345 | Only MiniMax M2.5 measured |
| LMArena Text: entertainment and media | Not measured | 1329 | Only MiniMax M2.5 measured |
| Neither model has a result on the other 25 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Ling 3.0 Flash | MiniMax M2.5 | 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
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 | inclusionAI Ling 3.0 Flash | MiniMax M2.5 |
|---|---|---|
| Maker | inclusionAI | MiniMax |
| Weights | Open weights | Open weights |
| Released | July 23, 2026 | February 12, 2026 |
| Context window | 262K | 205K |
| Input $/Mtok | $0.0405 | $0.30 |
| Output $/Mtok | $0.1215 | $1.20 |
| Coding index | 29.6 | 37.2 |
| Independent measurements behind it | 2 | 3 |
| Cost per solved task | $0.05 | $0.62 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Ling 3.0 Flash better than MiniMax M2.5 for coding?
Pick MiniMax M2.5 for coding: it scores 37.2 on the Vibe Coding Index against 29.6 for Ling 3.0 Flash. Pick Ling 3.0 Flash for output tokens 9.9x cheaper ($0.1215 vs $1.20 per million) and a lower cost per solved task ($0.05 against $0.62).
Which is cheaper, Ling 3.0 Flash or MiniMax M2.5?
Ling 3.0 Flash costs $0.0405 per million input tokens and $0.1215 per million output tokens; MiniMax M2.5 costs $0.30 and $1.20. Per solved SWE-bench Verified task, Ling 3.0 Flash costs $0.05 and MiniMax M2.5 $0.62.
Which has the bigger context window, Ling 3.0 Flash or MiniMax M2.5?
Ling 3.0 Flash, with 262K tokens against 205K.
How many benchmarks is the Ling 3.0 Flash vs MiniMax M2.5 comparison based on?
12 benchmarks have a result for both models as of October 23, 2026. Ling 3.0 Flash's coding index rests on 2 independent benchmarks and MiniMax M2.5's on 3. Where only one model has a result, the page says so instead of naming a winner.