Ling 3.0 Flash vs Mistral Medium 3.5
Ling 3.0 Flash leads Mistral Medium 3.5 on coding, 29.6 to 25.5 on the Vibe Coding Index, and Ling 3.0 Flash costs 61.7x less per output token ($0.1215 vs $7.50 per million). Updated October 22, 2026; 20 benchmarks have a result for both.
Ling 3.0 Flash or Mistral Medium 3.5: which to pick
Pick Ling 3.0 Flash for coding: it scores 29.6 on the Vibe Coding Index against 25.5 for Mistral Medium 3.5, though on 2 benchmarks against 3.
Pick Mistral Medium 3.5 for work measured by SWE-bench Verified (Vals AI) or Code migration (Vals AI), where it leads.
- Shared results
- 20
- Ling 3.0 Flash only
- 4
- Mistral Medium 3.5 only
- 61
- 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 25.5.
2 and 3 benchmarks behind the scores
Intelligence
AA Intelligence Index
Ling 3.0 Flash
Ling 3.0 Flash leads, 20.1 against 14.2.
Same board, both measured
Agentic work
AA Agentic Index
Ling 3.0 Flash
Ling 3.0 Flash leads, 19.3 against 7.0.
Same board, both measured
Reasoning
GPQA Diamond
Ling 3.0 Flash
Ling 3.0 Flash leads, 84.8% against 34.9%.
Same board, both measured
Knowledge
MMLU Pro
Ling 3.0 Flash
Ling 3.0 Flash leads, 82.0% against 75.3%.
Same board, both measured
Web design
Design Arena: websites
No call
Only Mistral Medium 3.5 has a Design Arena: websites result, 1142.
Directional only
Long documents
Listed context window
No call
Both take 262K tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Ling 3.0 Flash
Ling 3.0 Flash $0.0001, Mistral Medium 3.5 $0.0053 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
Ling 3.0 Flash
Ling 3.0 Flash $0.05, Mistral Medium 3.5 $6.02.
Published attempt cost and pass rate
At a glance
- Coding index
- Ling 3.0 Flash 29.6 from 2 benchmarks, Mistral Medium 3.5 25.5 from 3 benchmarks.
- Widest category
- Reasoning, 23.5 points apart on average over 3 shared benchmarks.
- Widest single gap
- GPQA Diamond: Ling 3.0 Flash 84.8% against Mistral Medium 3.5 34.9%, 50.0 points apart on a 0 to 100 scale.
- Output price
- Ling 3.0 Flash $0.1215 against Mistral Medium 3.5 $7.50 per million output tokens, 61.7x apart.
- Context window
- Both take 262K tokens.
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 | Mistral Medium 3.5 | Ties |
|---|---|---|---|---|
| Coding | 4 | 2 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 2 | 2 | ||
| Reasoning | 3 | 0 | ||
| Knowledge | 3 | 1 | ||
| General | 2 | 0 | ||
| All shared boards | 15 | 5 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index42
| Benchmark | Ling 3.0 Flash | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| LMArena WebDev | Not measured | 1264 | Only Mistral Medium 3.5 measured |
| AA Coding Index | 50.6 | 46.9 | Ling 3.0 Flash |
| LiveCodeBench (Vals AI) | 84.0% | Not measured | Only Ling 3.0 Flash measured |
| SWE-bench Verified (Vals AI) | 65.2% | 66.4% | Mistral Medium 3.5 |
| Vibe Code Bench (Vals AI) | 2.9% | 2.9% | Ling 3.0 Flash |
| Code migration (Vals AI) | 0.0% | 5.1% | Mistral Medium 3.5 |
| Terminal-Bench 2.1 (Vals AI) | 50.2% | 39.0% | Ling 3.0 Flash |
| SkillsBench (Vals AI) | 27.9% | Not measured | Only Ling 3.0 Flash measured |
| SciCode | 42.0% | 40.2% | Ling 3.0 Flash |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 33.3% | Only Mistral Medium 3.5 measured |
| ALE-Bench | Not measured | 764.0 | Only Mistral Medium 3.5 measured |
| LMArena Text: coding | Not measured | 1460 | Only Mistral Medium 3.5 measured |
| LMArena Text: software and IT | Not measured | 1458 | Only Mistral Medium 3.5 measured |
| LMArena WebDev: React | Not measured | 1254 | Only Mistral Medium 3.5 measured |
| LMArena WebDev: HTML | Not measured | 1308 | Only Mistral Medium 3.5 measured |
| Neither model has a result on the other 24 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Ling 3.0 Flash | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| AA Intelligence Index | 20.1 | 14.2 | Ling 3.0 Flash |
| Epoch Capabilities Index | Not measured | 141.4 | Only Mistral Medium 3.5 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index22
| Benchmark | Ling 3.0 Flash | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| AA Agentic Index | 19.3 | 7.0 | Ling 3.0 Flash |
| Finance Agent (Vals AI) | 30.3% | 32.1% | Mistral Medium 3.5 |
| Tax Agent Bench (Vals AI) | Not measured | 4.5% | Only Mistral Medium 3.5 measured |
| Legal Research Bench (Vals AI) | 0.0% | 9.1% | Mistral Medium 3.5 |
| Excel Modeling Benchmark (Vals AI) | 25.0% | 11.2% | Ling 3.0 Flash |
| τ²-Bench Telecom | Not measured | 94.2% | Only Mistral Medium 3.5 measured |
| Neither model has a result on the other 35 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | Ling 3.0 Flash | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| GPQA Diamond | 84.8% | 34.9% | Ling 3.0 Flash |
| GPQA Diamond (Artificial Analysis run) | 85.5% | 74.8% | Ling 3.0 Flash |
| Humanity's Last Exam (Artificial Analysis run) | 23.7% | 13.8% | Ling 3.0 Flash |
| LMArena Text: hard prompts | Not measured | 1436 | Only Mistral Medium 3.5 measured |
| LMArena Text: expert | Not measured | 1432 | Only Mistral Medium 3.5 measured |
| LMArena Text: hard prompts in English | Not measured | 1445 | Only Mistral Medium 3.5 measured |
| LMArena Vision: diagrams | Not measured | 1254 | Only Mistral Medium 3.5 measured |
| LMArena Vision: homework | Not measured | 1230 | Only Mistral Medium 3.5 measured |
| Kagi LLM Benchmark | Not measured | 41.4% | Only Mistral Medium 3.5 measured |
| Neither model has a result on the other 13 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | Ling 3.0 Flash | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| ProofBench | Not measured | 9.0% | Only Mistral Medium 3.5 measured |
| LMArena Text: math | Not measured | 1431 | Only Mistral Medium 3.5 measured |
| LMArena Text: mathematical work | Not measured | 1440 | Only Mistral Medium 3.5 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 | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| MMLU Pro | 82.0% | 75.3% | Ling 3.0 Flash |
| LegalBench (Vals AI) | 79.7% | Not measured | Only Ling 3.0 Flash measured |
| CaseLaw (Vals AI) | Not measured | 44.2% | Only Mistral Medium 3.5 measured |
| CorpFin (Vals AI) | 61.9% | 58.8% | Ling 3.0 Flash |
| TaxEval (Vals AI) | 70.7% | 68.0% | Ling 3.0 Flash |
| MortgageTax (Vals AI) | Not measured | 28.9% | Only Mistral Medium 3.5 measured |
| MedCode (Vals AI) | 32.3% | 33.8% | Mistral Medium 3.5 |
| LMArena Text: science | Not measured | 1428 | Only Mistral Medium 3.5 measured |
| LMArena Text: medicine and healthcare | Not measured | 1409 | Only Mistral Medium 3.5 measured |
| LMArena Text: legal and government | Not measured | 1437 | Only Mistral Medium 3.5 measured |
| LMArena Text: business and finance | Not measured | 1426 | Only Mistral Medium 3.5 measured |
| Neither model has a result on the other 7 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | Ling 3.0 Flash | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1142 | Only Mistral Medium 3.5 measured |
| Design Arena: UI components | Not measured | 1076 | Only Mistral Medium 3.5 measured |
| Design Arena: game dev | Not measured | 1079 | Only Mistral Medium 3.5 measured |
| Design Arena: data viz | Not measured | 1120 | Only Mistral Medium 3.5 measured |
| Design Arena: 3D | Not measured | 1060 | Only Mistral Medium 3.5 measured |
| Design Arena: all code categories | Not measured | 1121 | Only Mistral Medium 3.5 measured |
| Design Arena: SVG | Not measured | 949 | Only Mistral Medium 3.5 measured |
| Design Arena: ASCII art | Not measured | 1062 | Only Mistral Medium 3.5 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Ling 3.0 Flash | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| Public Benefits Bench (Vals AI) | 50.7% | Not measured | Only Ling 3.0 Flash measured |
| SAGE (Vals AI) | Not measured | 37.6% | Only Mistral Medium 3.5 measured |
| MedScribe (Vals AI) | 80.9% | 67.7% | Ling 3.0 Flash |
| Vals Multimodal Index | Not measured | 34.8% | Only Mistral Medium 3.5 measured |
| IFBench | Not measured | 68.8% | Only Mistral Medium 3.5 measured |
| Long context reasoning (AA-LCR) | 73.0% | 69.3% | Ling 3.0 Flash |
| DTBench | Not measured | 75.5% | Only Mistral Medium 3.5 measured |
| LMCA | Not measured | 26.1% | Only Mistral Medium 3.5 measured |
| Surface Evolver Bench | Not measured | 26.9% | Only Mistral Medium 3.5 measured |
| LMArena Text | Not measured | 1421 | Only Mistral Medium 3.5 measured |
| LMArena Text: instruction following | Not measured | 1415 | Only Mistral Medium 3.5 measured |
| LMArena Text: creative writing | Not measured | 1374 | Only Mistral Medium 3.5 measured |
| LMArena Vision | Not measured | 1222 | Only Mistral Medium 3.5 measured |
| LMArena Text: English | Not measured | 1433 | Only Mistral Medium 3.5 measured |
| LMArena Text: non-English | Not measured | 1404 | Only Mistral Medium 3.5 measured |
| LMArena Text: Chinese | Not measured | 1444 | Only Mistral Medium 3.5 measured |
| LMArena Text: German | Not measured | 1453 | Only Mistral Medium 3.5 measured |
| LMArena Text: Korean | Not measured | 1384 | Only Mistral Medium 3.5 measured |
| LMArena Text: French | Not measured | 1448 | Only Mistral Medium 3.5 measured |
| LMArena Text: Spanish | Not measured | 1411 | Only Mistral Medium 3.5 measured |
| LMArena Text: Russian | Not measured | 1395 | Only Mistral Medium 3.5 measured |
| LMArena Text: Polish | Not measured | 1430 | Only Mistral Medium 3.5 measured |
| LMArena Text: multi-turn | Not measured | 1423 | Only Mistral Medium 3.5 measured |
| LMArena Text: longer queries | Not measured | 1415 | Only Mistral Medium 3.5 measured |
| LMArena Text: writing and language | Not measured | 1384 | Only Mistral Medium 3.5 measured |
| LMArena Text: entertainment and media | Not measured | 1382 | Only Mistral Medium 3.5 measured |
| LMArena Vision: English | Not measured | 1240 | Only Mistral Medium 3.5 measured |
| LMArena Vision: Chinese | Not measured | 1244 | Only Mistral Medium 3.5 measured |
| LMArena Vision: reading text | Not measured | 1233 | Only Mistral Medium 3.5 measured |
| LMArena Vision: humor | Not measured | 1216 | Only Mistral Medium 3.5 measured |
| LMArena Vision: creative writing | Not measured | 1198 | Only Mistral Medium 3.5 measured |
| Neither model has a result on the other 15 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Ling 3.0 Flash | Mistral Medium 3.5 | 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 | Mistral Medium 3.5 |
|---|---|---|
| Maker | inclusionAI | Mistral AI |
| Weights | Open weights | No public weights listed |
| Released | July 23, 2026 | April 30, 2026 |
| Context window | 262K | 262K |
| Input $/Mtok | $0.0405 | $1.50 |
| Output $/Mtok | $0.1215 | $7.50 |
| Coding index | 29.6 | 25.5 |
| Independent measurements behind it | 2 | 3 |
| Cost per solved task | $0.05 | $6.02 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Ling 3.0 Flash better than Mistral Medium 3.5 for coding?
Pick Ling 3.0 Flash for coding: it scores 29.6 on the Vibe Coding Index against 25.5 for Mistral Medium 3.5, though on 2 benchmarks against 3. Pick Mistral Medium 3.5 for work measured by SWE-bench Verified (Vals AI) or Code migration (Vals AI), where it leads.
Which is cheaper, Ling 3.0 Flash or Mistral Medium 3.5?
Ling 3.0 Flash costs $0.0405 per million input tokens and $0.1215 per million output tokens; Mistral Medium 3.5 costs $1.50 and $7.50. Per solved SWE-bench Verified task, Ling 3.0 Flash costs $0.05 and Mistral Medium 3.5 $6.02.
Which has the bigger context window, Ling 3.0 Flash or Mistral Medium 3.5?
Neither: both take 262K tokens.
How many benchmarks is the Ling 3.0 Flash vs Mistral Medium 3.5 comparison based on?
20 benchmarks have a result for both models as of October 22, 2026. Ling 3.0 Flash's coding index rests on 2 independent benchmarks and Mistral Medium 3.5's on 3. Where only one model has a result, the page says so instead of naming a winner.