Qwen3.8 27B vs UnslopNemo 12B
UnslopNemo 12B costs 6.4x less per output token than Qwen3.8 27B ($0.40 vs $2.55 per million). Updated October 23, 2026; no benchmark has tested both models yet.
- Coding
- 74.1
- Overall
- 59.5
- Output $/M
- $2.55
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.40
Qwen3.8 27B or UnslopNemo 12B: which to pick
Only Qwen3.8 27B has a coding score, 74.1 on the Vibe Coding Index; UnslopNemo 12B has none, so the two cannot be ranked on coding.
Pick UnslopNemo 12B for output tokens 6.4x cheaper ($0.40 vs $2.55 per million) and a larger context window (1.02M against 1M tokens).
No benchmark has tested both yet. Qwen3.8 27B has results on LiveBench coding, LMArena WebDev and AA Coding Index; UnslopNemo 12B has none yet. Compare them on price and context instead.
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 Qwen3.8 27B has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
No call
Only Qwen3.8 27B has a AA Intelligence Index result, 33.7.
Directional only
Agentic work
AA Agentic Index
No call
Only Qwen3.8 27B has a AA Agentic Index result, 45.8.
Directional only
Reasoning
GPQA Diamond
No call
Only Qwen3.8 27B has a GPQA Diamond result, 88.9%.
Directional only
Knowledge
MMLU Pro
No call
Only Qwen3.8 27B has a MMLU Pro result, 84.3%.
Directional only
Long documents
Listed context window
UnslopNemo 12B
UnslopNemo 12B takes 1.02M tokens against 1M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
UnslopNemo 12B
Qwen3.8 27B $0.0017, UnslopNemo 12B $0.0006 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Qwen3.8 27B has the number, $1.26.
Directional only
At a glance
- Coding index
- Qwen3.8 27B 74.1 from 4 benchmarks, UnslopNemo 12B not benchmarked.
- Widest category
- None yet: no category has been tested on both models.
- Widest single gap
- None yet: no benchmark has tested both models.
- Output price
- Qwen3.8 27B $2.55 against UnslopNemo 12B $0.40 per million output tokens, 6.4x apart.
- Context window
- Qwen3.8 27B takes 1M tokens, UnslopNemo 12B 1.02M.
Benchmark by benchmark
Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.
CodingDirectional onlySix of these boards feed the Vibe Coding Index18 boards measured
| Benchmark | Qwen3.8 27B | UnslopNemo 12B | Leader |
|---|---|---|---|
| LiveBench coding | 86.8% | Not measured | Only Qwen3.8 27B measured |
| LMArena WebDev | 1591 | Not measured | Only Qwen3.8 27B measured |
| AA Coding Index | 68.1 | Not measured | Only Qwen3.8 27B measured |
| LiveCodeBench (Vals AI) | 84.0% | Not measured | Only Qwen3.8 27B measured |
| SWE-bench Verified (Vals AI) | 86.0% | Not measured | Only Qwen3.8 27B measured |
| Vibe Code Bench (Vals AI) | 64.8% | Not measured | Only Qwen3.8 27B measured |
| Code migration (Vals AI) | 14.2% | Not measured | Only Qwen3.8 27B measured |
| ProgramBench (Vals AI) | 0.0% | Not measured | Only Qwen3.8 27B measured |
| IOI (Vals AI) | 39.1% | Not measured | Only Qwen3.8 27B measured |
| Terminal-Bench 2.1 (Vals AI) | 58.4% | Not measured | Only Qwen3.8 27B measured |
| SkillsBench (Vals AI) | 38.1% | Not measured | Only Qwen3.8 27B measured |
| CyberBench (Vals AI) | 67.8% | Not measured | Only Qwen3.8 27B measured |
| SciCode | 46.6% | Not measured | Only Qwen3.8 27B measured |
| LMArena Text: coding | 1483 | Not measured | Only Qwen3.8 27B measured |
| LMArena Text: software and IT | 1477 | Not measured | Only Qwen3.8 27B measured |
| LMArena WebDev: React | 1600 | Not measured | Only Qwen3.8 27B measured |
| LMArena WebDev: HTML | 1569 | Not measured | Only Qwen3.8 27B measured |
| LMArena WebDev: from an image | 1577 | Not measured | Only Qwen3.8 27B measured |
| Neither model has a result on the other 25 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | Qwen3.8 27B | UnslopNemo 12B | Leader |
|---|---|---|---|
| AA Intelligence Index | 33.7 | Not measured | Only Qwen3.8 27B measured |
| Epoch Capabilities Index | 151.7 | Not measured | Only Qwen3.8 27B measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index10 boards measured
| Benchmark | Qwen3.8 27B | UnslopNemo 12B | Leader |
|---|---|---|---|
| AA Agentic Index | 45.8 | Not measured | Only Qwen3.8 27B measured |
| LiveBench research | 59.4% | Not measured | Only Qwen3.8 27B measured |
| LiveBench browser | 32.0% | Not measured | Only Qwen3.8 27B measured |
| LiveBench long agentic | 27.9% | Not measured | Only Qwen3.8 27B measured |
| Finance Agent (Vals AI) | 48.5% | Not measured | Only Qwen3.8 27B measured |
| Tax Agent Bench (Vals AI) | 30.9% | Not measured | Only Qwen3.8 27B measured |
| Legal Research Bench (Vals AI) | 36.1% | Not measured | Only Qwen3.8 27B measured |
| Excel Modeling Benchmark (Vals AI) | 59.7% | Not measured | Only Qwen3.8 27B measured |
| Terminal-Bench-Science (Vals AI) | 0.0% | Not measured | Only Qwen3.8 27B measured |
| APEX-Agents | 47.5% | Not measured | Only Qwen3.8 27B measured |
| Neither model has a result on the other 32 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index12 boards measured
| Benchmark | Qwen3.8 27B | UnslopNemo 12B | Leader |
|---|---|---|---|
| GPQA Diamond | 88.9% | Not measured | Only Qwen3.8 27B measured |
| LiveBench reasoning | 80.0% | Not measured | Only Qwen3.8 27B measured |
| MMMU Pro (Vals AI) | 83.9% | Not measured | Only Qwen3.8 27B measured |
| GPQA Diamond (Artificial Analysis run) | 90.5% | Not measured | Only Qwen3.8 27B measured |
| Humanity's Last Exam (Artificial Analysis run) | 33.9% | Not measured | Only Qwen3.8 27B measured |
| ARC-AGI | 87.5% | Not measured | Only Qwen3.8 27B measured |
| ARC-AGI-2 | 42.4% | Not measured | Only Qwen3.8 27B measured |
| LMArena Text: hard prompts | 1459 | Not measured | Only Qwen3.8 27B measured |
| LMArena Text: expert | 1480 | Not measured | Only Qwen3.8 27B measured |
| LMArena Text: hard prompts in English | 1462 | Not measured | Only Qwen3.8 27B measured |
| LMArena Vision: diagrams | 1285 | Not measured | Only Qwen3.8 27B measured |
| LMArena Vision: homework | 1295 | Not measured | Only Qwen3.8 27B measured |
| Neither model has a result on the other 11 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index3 boards measured
| Benchmark | Qwen3.8 27B | UnslopNemo 12B | Leader |
|---|---|---|---|
| ProofBench | 16.0% | Not measured | Only Qwen3.8 27B measured |
| LMArena Text: math | 1455 | Not measured | Only Qwen3.8 27B measured |
| LMArena Text: mathematical work | 1458 | Not measured | Only Qwen3.8 27B measured |
| Neither model has a result on the other 11 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index9 boards measured
| Benchmark | Qwen3.8 27B | UnslopNemo 12B | Leader |
|---|---|---|---|
| MMLU Pro | 84.3% | Not measured | Only Qwen3.8 27B measured |
| LegalBench (Vals AI) | 82.4% | Not measured | Only Qwen3.8 27B measured |
| TaxEval (Vals AI) | 70.8% | Not measured | Only Qwen3.8 27B measured |
| MortgageTax (Vals AI) | 64.9% | Not measured | Only Qwen3.8 27B measured |
| MedCode (Vals AI) | 28.7% | Not measured | Only Qwen3.8 27B measured |
| LMArena Text: science | 1468 | Not measured | Only Qwen3.8 27B measured |
| LMArena Text: medicine and healthcare | 1438 | Not measured | Only Qwen3.8 27B measured |
| LMArena Text: legal and government | 1447 | Not measured | Only Qwen3.8 27B measured |
| LMArena Text: business and finance | 1438 | Not measured | Only Qwen3.8 27B measured |
| Neither model has a result on the other 11 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Qwen3.8 27B | UnslopNemo 12B | Leader |
|---|---|---|---|
| LiveBench design | 75.9% | Not measured | Only Qwen3.8 27B measured |
| Neither model has a result on the other 8 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index30 boards measured
| Benchmark | Qwen3.8 27B | UnslopNemo 12B | Leader |
|---|---|---|---|
| LiveBench data analysis | 76.6% | Not measured | Only Qwen3.8 27B measured |
| SAGE (Vals AI) | 52.4% | Not measured | Only Qwen3.8 27B measured |
| MedScribe (Vals AI) | 83.8% | Not measured | Only Qwen3.8 27B measured |
| Long context reasoning (AA-LCR) | 82.0% | Not measured | Only Qwen3.8 27B measured |
| DTBench | 88.0% | Not measured | Only Qwen3.8 27B measured |
| LMCA | 41.4% | Not measured | Only Qwen3.8 27B measured |
| Surface Evolver Bench | 45.0% | Not measured | Only Qwen3.8 27B measured |
| LMArena Text | 1440 | Not measured | Only Qwen3.8 27B measured |
| LMArena Text: instruction following | 1436 | Not measured | Only Qwen3.8 27B measured |
| LMArena Text: creative writing | 1383 | Not measured | Only Qwen3.8 27B measured |
| LMArena Vision | 1269 | Not measured | Only Qwen3.8 27B measured |
| LMArena Text: English | 1448 | Not measured | Only Qwen3.8 27B measured |
| LMArena Text: non-English | 1428 | Not measured | Only Qwen3.8 27B measured |
| LMArena Text: Chinese | 1501 | Not measured | Only Qwen3.8 27B measured |
| LMArena Text: German | 1442 | Not measured | Only Qwen3.8 27B measured |
| LMArena Text: Japanese | 1388 | Not measured | Only Qwen3.8 27B measured |
| LMArena Text: Korean | 1380 | Not measured | Only Qwen3.8 27B measured |
| LMArena Text: French | 1464 | Not measured | Only Qwen3.8 27B measured |
| LMArena Text: Spanish | 1448 | Not measured | Only Qwen3.8 27B measured |
| LMArena Text: Russian | 1414 | Not measured | Only Qwen3.8 27B measured |
| LMArena Text: Polish | 1447 | Not measured | Only Qwen3.8 27B measured |
| LMArena Text: multi-turn | 1440 | Not measured | Only Qwen3.8 27B measured |
| LMArena Text: longer queries | 1449 | Not measured | Only Qwen3.8 27B measured |
| LMArena Text: writing and language | 1405 | Not measured | Only Qwen3.8 27B measured |
| LMArena Text: entertainment and media | 1393 | Not measured | Only Qwen3.8 27B measured |
| LMArena Vision: English | 1267 | Not measured | Only Qwen3.8 27B measured |
| LMArena Vision: Chinese | 1326 | Not measured | Only Qwen3.8 27B measured |
| LMArena Vision: reading text | 1281 | Not measured | Only Qwen3.8 27B measured |
| LMArena Vision: humor | 1235 | Not measured | Only Qwen3.8 27B measured |
| LMArena Vision: creative writing | 1237 | Not measured | Only Qwen3.8 27B measured |
| Neither model has a result on the other 18 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
UnslopNemo 12B costs less
Repository review
50K input + 3K output tokens
UnslopNemo 12B costs less
Agent loop
200K input + 10K output tokens
UnslopNemo 12B 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 | Qwen3.8 27B | TheDrummer UnslopNemo 12B |
|---|---|---|
| Maker | Qwen | TheDrummer |
| Weights | Open weights | Open weights |
| Released | August 14, 2026 | November 8, 2024 |
| Context window | 1M | 1.02M |
| Input $/Mtok | $0.425 | $0.40 |
| Output $/Mtok | $2.55 | $0.40 |
| Coding index | 74.1 | Not benchmarked |
| Independent measurements behind it | 4 | 0 |
| Cost per solved task | $1.26 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Qwen3.8 27B better than UnslopNemo 12B for coding?
Only Qwen3.8 27B has a coding score, 74.1 on the Vibe Coding Index; UnslopNemo 12B has none, so the two cannot be ranked on coding. Pick UnslopNemo 12B for output tokens 6.4x cheaper ($0.40 vs $2.55 per million) and a larger context window (1.02M against 1M tokens).
Which is cheaper, Qwen3.8 27B or UnslopNemo 12B?
Qwen3.8 27B costs $0.425 per million input tokens and $2.55 per million output tokens; UnslopNemo 12B costs $0.40 and $0.40. Cost per solved task is known for Qwen3.8 27B only.
Which has the bigger context window, Qwen3.8 27B or UnslopNemo 12B?
UnslopNemo 12B, with 1.02M tokens against 1M.
Has any benchmark tested both Qwen3.8 27B and UnslopNemo 12B?
No benchmark has tested both yet. Qwen3.8 27B has results on LiveBench coding, LMArena WebDev and AA Coding Index; UnslopNemo 12B has none yet. Compare them on price and context instead. Data as of October 23, 2026.