Qwen3.6 27B vs Hunyuan A13B Instruct
Hunyuan A13B Instruct costs 4.7x less per output token than Qwen3.6 27B ($0.57 vs $2.70 per million). Updated October 22, 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
- $0.57
Qwen3.6 27B or Hunyuan A13B Instruct: which to pick
Only Qwen3.6 27B has a coding score, 36.8 on the Vibe Coding Index; Hunyuan A13B Instruct has none, so the two cannot be ranked on coding.
Pick Hunyuan A13B Instruct for output tokens 4.7x cheaper ($0.57 vs $2.70 per million).
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); Hunyuan A13B Instruct 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
Qwen3.6 27B
Qwen3.6 27B takes 262K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Hunyuan A13B Instruct
Qwen3.6 27B $0.0018, Hunyuan A13B Instruct $0.0004 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, Hunyuan A13B Instruct 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 Hunyuan A13B Instruct $0.57 per million output tokens, 4.7x apart.
- Context window
- Qwen3.6 27B takes 262K tokens, Hunyuan A13B Instruct 131K.
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 | Hunyuan A13B Instruct | 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 | Hunyuan A13B Instruct | 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 | Hunyuan A13B Instruct | 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 | Hunyuan A13B Instruct | 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 | Hunyuan A13B Instruct | 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 | Hunyuan A13B Instruct | 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 | Hunyuan A13B Instruct | 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
Hunyuan A13B Instruct costs less
Repository review
50K input + 3K output tokens
Hunyuan A13B Instruct costs less
Agent loop
200K input + 10K output tokens
Hunyuan A13B Instruct 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 | Tencent Hunyuan A13B Instruct |
|---|---|---|
| Maker | Qwen | Tencent |
| Weights | Open weights | Open weights |
| Released | April 27, 2026 | July 8, 2025 |
| Context window | 262K | 131K |
| Input $/Mtok | $0.45 | $0.14 |
| Output $/Mtok | $2.70 | $0.57 |
| 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 Hunyuan A13B Instruct for coding?
Only Qwen3.6 27B has a coding score, 36.8 on the Vibe Coding Index; Hunyuan A13B Instruct has none, so the two cannot be ranked on coding. Pick Hunyuan A13B Instruct for output tokens 4.7x cheaper ($0.57 vs $2.70 per million).
Which is cheaper, Qwen3.6 27B or Hunyuan A13B Instruct?
Qwen3.6 27B costs $0.45 per million input tokens and $2.70 per million output tokens; Hunyuan A13B Instruct costs $0.14 and $0.57. Cost per solved task is known for Qwen3.6 27B only.
Which has the bigger context window, Qwen3.6 27B or Hunyuan A13B Instruct?
Qwen3.6 27B, with 262K tokens against 131K.
Has any benchmark tested both Qwen3.6 27B and Hunyuan A13B Instruct?
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); Hunyuan A13B Instruct has none yet. Compare them on price and context instead. Data as of October 22, 2026.