Qwen3.6 27B vs Pareto 26.9
Qwen3.6 27B costs 2.8x less per output token than Pareto 26.9 ($2.70 vs $7.50 per million). Updated October 23, 2026; no benchmark has tested both models yet.
- Coding
- 36.8
- Overall
- 45.3
- Output $/M
- $2.70
- Coding
- No data
- Overall
- No data
- Output $/M
- $7.50
Qwen3.6 27B or Pareto 26.9: which to pick
Only Qwen3.6 27B has a coding score, 36.8 on the Vibe Coding Index; Pareto 26.9 has none, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
No benchmark has tested both yet. Qwen3.6 27B has results on AA Coding Index, SWE-bench Verified (Vals AI) and Vibe Code Bench (Vals AI); Pareto 26.9 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 Qwen3.6 27B has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
No call
Only Qwen3.6 27B has a AA Intelligence Index result, 21.4.
Directional only
Agentic work
AA Agentic Index
No call
Only Qwen3.6 27B has a AA Agentic Index result, 18.5.
Directional only
Long documents
Listed context window
No call
Both take 262K tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.6 27B
Qwen3.6 27B $0.0018, Pareto 26.9 $0.0063 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Qwen3.6 27B has the number, $1.30.
Directional only
At a glance
- Coding index
- Qwen3.6 27B 36.8 from 2 benchmarks, Pareto 26.9 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
- Qwen3.6 27B $2.70 against Pareto 26.9 $7.50 per million output tokens, 2.8x apart.
- Context window
- Both take 262K tokens.
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 Index5 boards measured
| Benchmark | Qwen3.6 27B | Pareto 26.9 | Leader |
|---|---|---|---|
| AA Coding Index | 53.7 | Not measured | Only Qwen3.6 27B measured |
| SWE-bench Verified (Vals AI) | 70.0% | Not measured | Only Qwen3.6 27B measured |
| Vibe Code Bench (Vals AI) | 11.9% | Not measured | Only Qwen3.6 27B measured |
| SciCode | 42.8% | Not measured | Only Qwen3.6 27B measured |
| Terminal-Bench Hard (Artificial Analysis run) | 34.8% | Not measured | Only Qwen3.6 27B measured |
| Neither model has a result on the other 38 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | Qwen3.6 27B | Pareto 26.9 | Leader |
|---|---|---|---|
| AA Intelligence Index | 21.4 | Not measured | Only Qwen3.6 27B measured |
| Epoch Capabilities Index | 146.2 | Not measured | Only Qwen3.6 27B measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | Qwen3.6 27B | Pareto 26.9 | Leader |
|---|---|---|---|
| AA Agentic Index | 18.5 | Not measured | Only Qwen3.6 27B measured |
| τ²-Bench Telecom | 94.2% | Not measured | Only Qwen3.6 27B measured |
| Neither model has a result on the other 40 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured
| Benchmark | Qwen3.6 27B | Pareto 26.9 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 84.2% | Not measured | Only Qwen3.6 27B measured |
| Humanity's Last Exam (Artificial Analysis run) | 23.1% | Not measured | Only Qwen3.6 27B measured |
| GPQA Diamond (Epoch AI run) | 85.9% | Not measured | Only Qwen3.6 27B measured |
| Mystery game puzzles | 7.0% | Not measured | Only Qwen3.6 27B measured |
| Neither model has a result on the other 19 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | Qwen3.6 27B | Pareto 26.9 | Leader |
|---|---|---|---|
| OTIS Mock AIME | 91.1% | Not measured | Only Qwen3.6 27B measured |
| FrontierMath, tiers 1 to 3 | 35.1% | Not measured | Only Qwen3.6 27B measured |
| Neither model has a result on the other 12 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured
| Benchmark | Qwen3.6 27B | Pareto 26.9 | Leader |
|---|---|---|---|
| CaseLaw (Vals AI) | 53.2% | Not measured | Only Qwen3.6 27B measured |
| CorpFin (Vals AI) | 62.3% | Not measured | Only Qwen3.6 27B measured |
| TaxEval (Vals AI) | 71.3% | Not measured | Only Qwen3.6 27B measured |
| MortgageTax (Vals AI) | 68.3% | Not measured | Only Qwen3.6 27B measured |
| Neither model has a result on the other 16 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index6 boards measured
| Benchmark | Qwen3.6 27B | Pareto 26.9 | Leader |
|---|---|---|---|
| SAGE (Vals AI) | 45.6% | Not measured | Only Qwen3.6 27B measured |
| IFBench | 67.6% | Not measured | Only Qwen3.6 27B measured |
| Long context reasoning (AA-LCR) | 77.3% | Not measured | Only Qwen3.6 27B measured |
| DTBench | 78.1% | Not measured | Only Qwen3.6 27B measured |
| LMCA | 34.5% | Not measured | Only Qwen3.6 27B measured |
| EQ-Bench 4 | 1026 | Not measured | Only Qwen3.6 27B measured |
| Neither model has a result on the other 42 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
Qwen3.6 27B costs less
Repository review
50K input + 3K output tokens
Qwen3.6 27B costs less
Agent loop
200K input + 10K output tokens
Qwen3.6 27B 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 | Qwen3.6 27B | Pareto 26.9 |
|---|---|---|
| Maker | Qwen | Unbiased |
| Weights | Open weights | No public weights listed |
| Released | April 27, 2026 | October 13, 2026 |
| Context window | 262K | 262K |
| Input $/Mtok | $0.45 | $2.50 |
| Output $/Mtok | $2.70 | $7.50 |
| Coding index | 36.8 | Not benchmarked |
| Independent measurements behind it | 2 | 0 |
| Cost per solved task | $1.30 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Qwen3.6 27B better than Pareto 26.9 for coding?
Only Qwen3.6 27B has a coding score, 36.8 on the Vibe Coding Index; Pareto 26.9 has none, so the two cannot be ranked on coding. Compare them on price, context and the boards below.
Which is cheaper, Qwen3.6 27B or Pareto 26.9?
Qwen3.6 27B costs $0.45 per million input tokens and $2.70 per million output tokens; Pareto 26.9 costs $2.50 and $7.50. Cost per solved task is known for Qwen3.6 27B only.
Which has the bigger context window, Qwen3.6 27B or Pareto 26.9?
Neither: both take 262K tokens.
Has any benchmark tested both Qwen3.6 27B and Pareto 26.9?
No benchmark has tested both yet. Qwen3.6 27B has results on AA Coding Index, SWE-bench Verified (Vals AI) and Vibe Code Bench (Vals AI); Pareto 26.9 has none yet. Compare them on price and context instead. Data as of October 23, 2026.