gpt-oss-120b vs Ling 3.0 Flash
Ling 3.0 Flash leads gpt-oss-120b on the Artificial Analysis Intelligence Index, 20.1 to 11.6, and Ling 3.0 Flash costs 4.1x less per output token ($0.1215 vs $0.50 per million). Updated October 22, 2026; 14 benchmarks have a result for both.
gpt-oss-120b or Ling 3.0 Flash: which to pick
Only Ling 3.0 Flash has a coding score, 29.6 on the Vibe Coding Index; gpt-oss-120b has none, so the two cannot be ranked on coding.
Pick gpt-oss-120b for work measured by TaxEval (Vals AI), where it leads.
- Shared results
- 14
- gpt-oss-120b only
- 71
- Ling 3.0 Flash only
- 10
- 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
No call
Only Ling 3.0 Flash has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
Ling 3.0 Flash
Ling 3.0 Flash leads, 20.1 against 11.6.
Same board, both measured
Agentic work
AA Agentic Index
Ling 3.0 Flash
Ling 3.0 Flash leads, 19.3 against 3.7.
Same board, both measured
Reasoning
GPQA Diamond
Ling 3.0 Flash
Ling 3.0 Flash leads, 84.8% against 78.5%.
Same board, both measured
Math
AIME
No call
Only gpt-oss-120b has a AIME result, 92.6%.
Directional only
Knowledge
MMLU Pro
Ling 3.0 Flash
Ling 3.0 Flash leads, 82.0% against 79.2%.
Same board, both measured
Web design
Design Arena: websites
No call
Only gpt-oss-120b has a Design Arena: websites result, 968.
Directional only
Long documents
Listed context window
Ling 3.0 Flash
Ling 3.0 Flash takes 262K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Ling 3.0 Flash
gpt-oss-120b $0.0003, Ling 3.0 Flash $0.0001 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
Ling 3.0 Flash
gpt-oss-120b $0.51, Ling 3.0 Flash $0.05.
Published attempt cost and pass rate
At a glance
- Coding index
- gpt-oss-120b not benchmarked, Ling 3.0 Flash 29.6 from 2 benchmarks.
- Widest category
- General, 21.0 points apart on average over 1 shared benchmark.
- Widest single gap
- SWE-bench Verified (Vals AI): gpt-oss-120b 33.6% against Ling 3.0 Flash 65.2%, 31.6 points apart on a 0 to 100 scale.
- Output price
- gpt-oss-120b $0.50 against Ling 3.0 Flash $0.1215 per million output tokens, 4.1x apart.
- Context window
- gpt-oss-120b takes 131K 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 | gpt-oss-120b | Bars | Ling 3.0 Flash | Ties |
|---|---|---|---|---|
| Coding | 0 | 4 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 3 | ||
| Knowledge | 1 | 3 | ||
| General | 0 | 1 | ||
| All shared boards | 1 | 13 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index04
| Benchmark | gpt-oss-120b | Ling 3.0 Flash | Leader |
|---|---|---|---|
| Aider polyglot | 41.8% | Not measured | Only gpt-oss-120b measured |
| SWE-bench Verified | 26.0% | Not measured | Only gpt-oss-120b measured |
| AA Coding Index | 30.4 | 50.6 | Ling 3.0 Flash |
| LiveCodeBench (Vals AI) | 83.2% | 84.0% | Ling 3.0 Flash |
| SWE-bench Verified (Vals AI) | 33.6% | 65.2% | Ling 3.0 Flash |
| Vibe Code Bench (Vals AI) | Not measured | 2.9% | Only Ling 3.0 Flash measured |
| Code migration (Vals AI) | Not measured | 0.0% | Only Ling 3.0 Flash measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 50.2% | Only Ling 3.0 Flash measured |
| SkillsBench (Vals AI) | Not measured | 27.9% | Only Ling 3.0 Flash measured |
| LiveCodeBench (Artificial Analysis run) | 87.8% | Not measured | Only gpt-oss-120b measured |
| SciCode | 34.0% | 42.0% | Ling 3.0 Flash |
| Terminal-Bench Hard (Artificial Analysis run) | 23.5% | Not measured | Only gpt-oss-120b measured |
| Terminal-Bench | 18.7% | Not measured | Only gpt-oss-120b measured |
| WeirdML | 48.2% | Not measured | Only gpt-oss-120b measured |
| ALE-Bench | 575.6 | Not measured | Only gpt-oss-120b measured |
| LMArena Text: coding | 1379 | Not measured | Only gpt-oss-120b measured |
| LMArena Text: software and IT | 1387 | Not measured | Only gpt-oss-120b measured |
| SciCode | 7.7% | Not measured | Only gpt-oss-120b measured |
| Neither model has a result on the other 25 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | gpt-oss-120b | Ling 3.0 Flash | Leader |
|---|---|---|---|
| AA Intelligence Index | 11.6 | 20.1 | Ling 3.0 Flash |
| Epoch Capabilities Index | 139.8 | Not measured | Only gpt-oss-120b measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | gpt-oss-120b | Ling 3.0 Flash | Leader |
|---|---|---|---|
| AA Agentic Index | 3.7 | 19.3 | Ling 3.0 Flash |
| Finance Agent (Vals AI) | Not measured | 30.3% | Only Ling 3.0 Flash measured |
| Legal Research Bench (Vals AI) | Not measured | 0.0% | Only Ling 3.0 Flash measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 25.0% | Only Ling 3.0 Flash measured |
| τ²-Bench Telecom | 65.8% | Not measured | Only gpt-oss-120b measured |
| APEX-Agents | 4.4% | Not measured | Only gpt-oss-120b measured |
| METR time horizons | 56.6% | Not measured | Only gpt-oss-120b measured |
| Vending-Bench 2 | -$22 | Not measured | Only gpt-oss-120b measured |
| BrowseComp-Plus | 67.0% | Not measured | Only gpt-oss-120b measured |
| Neither model has a result on the other 33 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | gpt-oss-120b | Ling 3.0 Flash | Leader |
|---|---|---|---|
| GPQA Diamond | 78.5% | 84.8% | Ling 3.0 Flash |
| GPQA Diamond (Artificial Analysis run) | 78.2% | 85.5% | Ling 3.0 Flash |
| Humanity's Last Exam (Artificial Analysis run) | 19.6% | 23.7% | Ling 3.0 Flash |
| GPQA Diamond (Epoch AI run) | 75.8% | Not measured | Only gpt-oss-120b measured |
| SimpleBench | 22.1% | Not measured | Only gpt-oss-120b measured |
| Mystery game puzzles | 2.0% | Not measured | Only gpt-oss-120b measured |
| LMArena Text: hard prompts | 1364 | Not measured | Only gpt-oss-120b measured |
| LMArena Text: expert | 1356 | Not measured | Only gpt-oss-120b measured |
| LMArena Text: hard prompts in English | 1368 | Not measured | Only gpt-oss-120b measured |
| Kagi LLM Benchmark | 58.6% | Not measured | Only gpt-oss-120b measured |
| Neither model has a result on the other 13 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | gpt-oss-120b | Ling 3.0 Flash | Leader |
|---|---|---|---|
| AIME | 92.6% | Not measured | Only gpt-oss-120b measured |
| MATH 500 (Vals AI) | 94.8% | Not measured | Only gpt-oss-120b measured |
| MGSM (Vals AI) | 92.0% | Not measured | Only gpt-oss-120b measured |
| AA Math Index | 93.4 | Not measured | Only gpt-oss-120b measured |
| AIME 2025 (Artificial Analysis run) | 93.4% | Not measured | Only gpt-oss-120b measured |
| OTIS Mock AIME | 88.9% | Not measured | Only gpt-oss-120b measured |
| LMArena Text: math | 1389 | Not measured | Only gpt-oss-120b measured |
| LMArena Text: mathematical work | 1386 | Not measured | Only gpt-oss-120b measured |
| MathArena | 15.1% | Not measured | Only gpt-oss-120b measured |
| Neither model has a result on the other 5 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index13
| Benchmark | gpt-oss-120b | Ling 3.0 Flash | Leader |
|---|---|---|---|
| MMLU Pro | 79.2% | 82.0% | Ling 3.0 Flash |
| LegalBench (Vals AI) | 75.9% | 79.7% | Ling 3.0 Flash |
| CaseLaw (Vals AI) | 48.8% | Not measured | Only gpt-oss-120b measured |
| CorpFin (Vals AI) | 58.2% | 61.9% | Ling 3.0 Flash |
| TaxEval (Vals AI) | 71.6% | 70.7% | gpt-oss-120b |
| MedQA (Vals AI) | 91.4% | Not measured | Only gpt-oss-120b measured |
| MedCode (Vals AI) | Not measured | 32.3% | Only Ling 3.0 Flash measured |
| MMLU Pro (Artificial Analysis run) | 80.8% | Not measured | Only gpt-oss-120b measured |
| LMArena Text: science | 1382 | Not measured | Only gpt-oss-120b measured |
| LMArena Text: medicine and healthcare | 1388 | Not measured | Only gpt-oss-120b measured |
| LMArena Text: legal and government | 1355 | Not measured | Only gpt-oss-120b measured |
| LMArena Text: business and finance | 1359 | Not measured | Only gpt-oss-120b measured |
| PRBench Finance | 43.8% | Not measured | Only gpt-oss-120b measured |
| PRBench Legal | 40.2% | Not measured | Only gpt-oss-120b measured |
| Neither model has a result on the other 6 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | gpt-oss-120b | Ling 3.0 Flash | Leader |
|---|---|---|---|
| Design Arena: websites | 968 | Not measured | Only gpt-oss-120b measured |
| Design Arena: UI components | 924 | Not measured | Only gpt-oss-120b measured |
| Design Arena: game dev | 991 | Not measured | Only gpt-oss-120b measured |
| Design Arena: data viz | 987 | Not measured | Only gpt-oss-120b measured |
| Design Arena: 3D | 901 | Not measured | Only gpt-oss-120b measured |
| Design Arena: all code categories | 962 | Not measured | Only gpt-oss-120b measured |
| Neither model has a result on the other 3 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | gpt-oss-120b | Ling 3.0 Flash | Leader |
|---|---|---|---|
| Public Benefits Bench (Vals AI) | Not measured | 50.7% | Only Ling 3.0 Flash measured |
| MedScribe (Vals AI) | Not measured | 80.9% | Only Ling 3.0 Flash measured |
| IFBench | 69.0% | Not measured | Only gpt-oss-120b measured |
| Long context reasoning (AA-LCR) | 52.0% | 73.0% | Ling 3.0 Flash |
| DTBench | 76.3% | Not measured | Only gpt-oss-120b measured |
| LMCA | 22.1% | Not measured | Only gpt-oss-120b measured |
| Fiction.LiveBench | 44.4% | Not measured | Only gpt-oss-120b measured |
| Creative writing (Lech Mazur) | 77.1% | Not measured | Only gpt-oss-120b measured |
| Surface Evolver Bench | 25.0% | Not measured | Only gpt-oss-120b measured |
| LMArena Text | 1366 | Not measured | Only gpt-oss-120b measured |
| LMArena Text: instruction following | 1318 | Not measured | Only gpt-oss-120b measured |
| LMArena Text: creative writing | 1274 | Not measured | Only gpt-oss-120b measured |
| LMArena Text: English | 1375 | Not measured | Only gpt-oss-120b measured |
| LMArena Text: non-English | 1351 | Not measured | Only gpt-oss-120b measured |
| LMArena Text: Chinese | 1385 | Not measured | Only gpt-oss-120b measured |
| LMArena Text: German | 1353 | Not measured | Only gpt-oss-120b measured |
| LMArena Text: Japanese | 1331 | Not measured | Only gpt-oss-120b measured |
| LMArena Text: Korean | 1282 | Not measured | Only gpt-oss-120b measured |
| LMArena Text: French | 1370 | Not measured | Only gpt-oss-120b measured |
| LMArena Text: Spanish | 1389 | Not measured | Only gpt-oss-120b measured |
| LMArena Text: Russian | 1343 | Not measured | Only gpt-oss-120b measured |
| LMArena Text: Polish | 1353 | Not measured | Only gpt-oss-120b measured |
| LMArena Text: multi-turn | 1340 | Not measured | Only gpt-oss-120b measured |
| LMArena Text: longer queries | 1319 | Not measured | Only gpt-oss-120b measured |
| LMArena Text: writing and language | 1306 | Not measured | Only gpt-oss-120b measured |
| LMArena Text: entertainment and media | 1292 | Not measured | Only gpt-oss-120b measured |
| MultiChallenge | 45.3% | Not measured | Only gpt-oss-120b measured |
| Neither model has a result on the other 21 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | gpt-oss-120b | 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 | OpenAI gpt-oss-120b | inclusionAI Ling 3.0 Flash |
|---|---|---|
| Maker | OpenAI | inclusionAI |
| Weights | Open weights | Open weights |
| Released | August 5, 2025 | July 23, 2026 |
| Context window | 131K | 262K |
| Input $/Mtok | $0.10 | $0.0405 |
| Output $/Mtok | $0.50 | $0.1215 |
| Coding index | Not benchmarked | 29.6 |
| Independent measurements behind it | 1 | 2 |
| Cost per solved task | $0.51 | $0.05 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is gpt-oss-120b better than Ling 3.0 Flash for coding?
Only Ling 3.0 Flash has a coding score, 29.6 on the Vibe Coding Index; gpt-oss-120b has none, so the two cannot be ranked on coding. Pick gpt-oss-120b for work measured by TaxEval (Vals AI), where it leads.
Which is cheaper, gpt-oss-120b or Ling 3.0 Flash?
gpt-oss-120b costs $0.10 per million input tokens and $0.50 per million output tokens; Ling 3.0 Flash costs $0.0405 and $0.1215. Per solved SWE-bench Verified task, gpt-oss-120b costs $0.51 and Ling 3.0 Flash $0.05.
Which has the bigger context window, gpt-oss-120b or Ling 3.0 Flash?
Ling 3.0 Flash, with 262K tokens against 131K.
How many benchmarks is the gpt-oss-120b vs Ling 3.0 Flash comparison based on?
14 benchmarks have a result for both models as of October 22, 2026. gpt-oss-120b's coding index rests on 1 independent benchmark and Ling 3.0 Flash's on 2. Where only one model has a result, the page says so instead of naming a winner.