Model comparison
Qwen3.5-Flash vs Qwen3.8 27B
Qwen3.8 27B is the stronger model overall, scoring 45.3 to 42.3 on the Noometry Index. Qwen3.5-Flash costs 6.4× less per token, which makes it the better buy when Qwen3.8 27B's lead doesn't matter for your workload.
Last verified . 21 shared benchmarks.
Alibaba (Qwen)Summary
- They share 21 benchmarks with published results for both. Qwen3.5-Flash scores higher in 3 categories and Qwen3.8 27B in 5 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in coding, where Qwen3.8 27B leads 50.3 to 33.9.
- The biggest single-benchmark swing is LMCA: 29.1% for Qwen3.5-Flash and 41.4% for Qwen3.8 27B.
- Qwen3.5-Flash is cheaper at $0.10 / $0.40 per million input/output tokens, against $0.99 / $1.49 for Qwen3.8 27B.
- Qwen3.5-Flash accepts more context: 1M tokens versus 262K.
- Qwen3.8 27B has downloadable open weights; the other is API-only.
Side by side
| Qwen3.5-Flash | Qwen3.8 27B | |
|---|---|---|
| Provider | Alibaba (Qwen) | Alibaba (Qwen) |
| Noometry Index | 42.3 | 45.3 |
| Released | 2026-02-23 | 2026-08-14 |
| Weights | Proprietary | Open |
| Context window | 1M | 262K |
| Max output | 66K | 33K |
| Input $ / M tokens | $0.10 | $0.99 |
| Output $ / M tokens | $0.40 | $1.49 |
| Results tracked | 32 | 28 |
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Category by category
Coding Qwen3.8 27B leads
Qwen3.5-Flash: 33.9 (#234), Qwen3.8 27B: 50.3 (#42)
| Benchmark | Qwen3.5-Flash | Qwen3.8 27B |
|---|---|---|
| LMArena WebDev | 1243 | 1592 |
| LMArena Coding | 1412 | 1487 |
| SciCode | — | 46.6% |
| ALE-Bench | 221.8 | — |
Agentic & Tool Use Not comparable
Qwen3.5-Flash: —, Qwen3.8 27B: —
| Benchmark | Qwen3.5-Flash | Qwen3.8 27B |
|---|---|---|
| Vending-Bench 2 | 462.69 | — |
Reasoning Qwen3.5-Flash leads
Qwen3.5-Flash: 32.9 (#63), Qwen3.8 27B: 31.5 (#71)
| Benchmark | Qwen3.5-Flash | Qwen3.8 27B |
|---|---|---|
| LMArena Hard Prompts | 1404 | 1459 |
| DTBench | 82.9% | 88% |
| LMCA | 29.1% | 41.4% |
| Epoch Capabilities Index | 143.98 | 149.38 |
| NYT Connections (extended) | — | 54.5% |
| CritPt | — | 5.4% |
| Chess Puzzles | 21% | — |
| Mystery Game Puzzles | 20% | — |
| Surface Evolver Bench | — | 45% |
Math Too close to call
Qwen3.5-Flash: 36.3 (#117), Qwen3.8 27B: 35.7 (#139)
| Benchmark | Qwen3.5-Flash | Qwen3.8 27B |
|---|---|---|
| LMArena Math | 1407 | 1453 |
| FrontierMath (Tiers 1-3) | 18.2% | — |
| OTIS Mock AIME 2024-2025 | 84.4% | — |
| ProofBench | — | 16% |
| FrontierMath (Feb 2025 set) | 6.2% | — |
| FrontierMath Tier 4 (v1) | 0% | — |
Knowledge Qwen3.5-Flash leads
Qwen3.5-Flash: 43.2 (#84), Qwen3.8 27B: 40.9 (#105)
| Benchmark | Qwen3.5-Flash | Qwen3.8 27B |
|---|---|---|
| LMArena Expert | 1408 | 1482 |
| GPQA Diamond | 82.3% | — |
| SimpleQA Verified | 20.3% | — |
| Vectara Hallucination Rate | 10.5% | — |
Multimodal Not comparable
Qwen3.5-Flash: —, Qwen3.8 27B: 40.5 (#36)
| Benchmark | Qwen3.5-Flash | Qwen3.8 27B |
|---|---|---|
| LMArena Vision | — | 1270 |
Multilingual Qwen3.8 27B leads
Qwen3.5-Flash: 50.7 (#116), Qwen3.8 27B: 54.0 (#54)
| Benchmark | Qwen3.5-Flash | Qwen3.8 27B |
|---|---|---|
| LMArena Non-English | 1385 | 1431 |
| LMArena Chinese | 1446 | 1504 |
| LMArena French | 1412 | 1468 |
| LMArena German | 1389 | 1435 |
| LMArena Japanese | 1369 | 1392 |
| LMArena Korean | 1344 | 1389 |
| LMArena Russian | 1379 | 1421 |
| LMArena Spanish | 1399 | 1444 |
Instruction Following Qwen3.8 27B leads
Qwen3.5-Flash: 72.8 (#133), Qwen3.8 27B: 75.8 (#54)
| Benchmark | Qwen3.5-Flash | Qwen3.8 27B |
|---|---|---|
| LMArena Instruction Following | 1374 | 1436 |
Long Context Qwen3.8 27B leads
Qwen3.5-Flash: 42.4 (#119), Qwen3.8 27B: 44.3 (#66)
| Benchmark | Qwen3.5-Flash | Qwen3.8 27B |
|---|---|---|
| LMArena Longer Query | 1392 | 1448 |
Writing & Preference Qwen3.8 27B leads
Qwen3.5-Flash: 58.2 (#117), Qwen3.8 27B: 65.9 (#41)
| Benchmark | Qwen3.5-Flash | Qwen3.8 27B |
|---|---|---|
| LMArena Text | 1397 | 1441 |
| LMArena Creative Writing | 1343 | 1382 |
| LMArena Multi-Turn | 1393 | 1443 |
| EQ-Bench Creative Writing | — | 1671 |
Frequently asked questions
Is Qwen3.5-Flash better than Qwen3.8 27B?
Qwen3.8 27B is the stronger model overall, scoring 45.3 to 42.3 on the Noometry Index. Qwen3.5-Flash costs 6.4× less per token, which makes it the better buy when Qwen3.8 27B's lead doesn't matter for your workload.
Which is cheaper, Qwen3.5-Flash or Qwen3.8 27B?
Qwen3.5-Flash is cheaper. It lists at $0.10 per million input tokens and $0.40 per million output tokens; Qwen3.8 27B lists at $0.99 and $1.49.
Is Qwen3.5-Flash or Qwen3.8 27B better for coding?
Qwen3.8 27B scores higher on coding benchmarks: 50.3 versus 33.9 in the Noometry coding category.
Which has the bigger context window?
Qwen3.5-Flash does, with 1M tokens against 262K.
How many benchmarks do Qwen3.5-Flash and Qwen3.8 27B share?
21 benchmarks have published results for both models. Qwen3.5-Flash has 32 scored results on Noometry and Qwen3.8 27B has 28.