vs
1:1
overall capability wins (0 ties across 2 scored). Qwen3.6 27B leads overall.
- Specs1:1
Verdict
Qwen3.7 Flash offers ~3.8x larger context (1M vs 262K); Qwen3.6 27B ships open weights (Apache 2.0).
- Qwen3.7 Flash offers ~3.8x larger context (1M vs 262K)
- Qwen3.6 27B ships open weights (Apache 2.0)
Cost for 1M in + 250K out
Illustrative chat workload at primary-provider list prices.
- Qwen3.6 27B
- -
- Qwen3.7 Flash
- $0.06
Price per 1M tokens
Full comparison
Organization
Alibaba
Alibaba
Family
Qwen
Qwen
License
Apache 2.0
Proprietary
Open weights
Yes
No
Release
Apr 22, 2026
Jul 27, 2026
Knowledge cutoff
-
-
API / provider
No primary API price listed
Alibaba Cloud
Modalities
text, image, video → text
text, image, video → text
Specs
Context window
262K
1M
Max output
82K
66K
Parameters
27B
—
Pricing
Input $/1M
—
$0.03
Output $/1M
—
$0.13
Blended $/1M (3∶1)
-
$0.06
Speed
tok/s
-
140
TTFT (s)
-
0.25
Reasoning
MMLU-Pro
—
—
GPQA Diamond
84.2
—
Humanity's Last Exam
23.1
—
AIME 2025
—
—
MATH-500
—
—
Humanity's Last Exam (with tools)
—
—
AA-Omniscience Accuracy
19.6
—
AA-LCR v1.1
77.3
—
CritPt
1.1
—
MMMU-Pro
74.6
—
IFBench
67.6
—
Chartography
—
—
Chartography (With Tools)
—
—
Coding
SWE-bench Verified
—
—
SWE-bench Pro
—
—
SWE-bench Multilingual
—
—
LiveCodeBench
—
—
Terminal-Bench 2.1
60.7
—
Aider Polyglot
—
—
Terminal-Bench 3
—
—
BigCodeBench
—
—
SciCode
42.8
—
CursorBench
—
—
SWE-Rebench
—
—
NL2Repo-Bench
—
—
DeepSWE
—
—
WebDev Arena
—
—
Terminal-Bench 4.0
—
—
CursorBench 4.0
—
—
FrontierCode v1.1 (Main)
—
—
Arena
LMArena Elo
—
—
| Metric | Qwen3.6 27B | Qwen3.7 Flash | Delta |
|---|---|---|---|
| Identity | |||
| Organization | Alibaba | Alibaba | — |
| Family | Qwen | Qwen | — |
| License | Apache 2.0 | Proprietary | — |
| Open weights | Yes | No | — |
| Release | Apr 22, 2026 | Jul 27, 2026 | — |
| Knowledge cutoff | - | - | — |
| API / provider | No primary API price listed | Alibaba Cloud | — |
| Modalities | text, image, video → text | text, image, video → text | — |
| Specs | |||
| Context window | 262K | 1M | B +738K |
| Max output | 82K | 66K | A +16K |
| Parameters | 27B | — | — |
| Pricing | |||
| Input $/1M | — | $0.03 | — |
| Output $/1M | — | $0.13 | — |
| Blended $/1M (3∶1) | - | $0.06 | — |
| Speed | |||
| tok/s | - | 140 | — |
| TTFT (s) | - | 0.25 | — |
| Reasoning | |||
| MMLU-Pro | — | — | — |
| GPQA Diamond | 84.2 | — | — |
| Humanity's Last Exam | 23.1 | — | — |
| AIME 2025 | — | — | — |
| MATH-500 | — | — | — |
| Humanity's Last Exam (with tools) | — | — | — |
| AA-Omniscience Accuracy | 19.6 | — | — |
| AA-LCR v1.1 | 77.3 | — | — |
| CritPt | 1.1 | — | — |
| MMMU-Pro | 74.6 | — | — |
| IFBench | 67.6 | — | — |
| Chartography | — | — | — |
| Chartography (With Tools) | — | — | — |
| Coding | |||
| SWE-bench Verified | — | — | — |
| SWE-bench Pro | — | — | — |
| SWE-bench Multilingual | — | — | — |
| LiveCodeBench | — | — | — |
| Terminal-Bench 2.1 | 60.7 | — | — |
| Aider Polyglot | — | — | — |
| Terminal-Bench 3 | — | — | — |
| BigCodeBench | — | — | — |
| SciCode | 42.8 | — | — |
| CursorBench | — | — | — |
| SWE-Rebench | — | — | — |
| NL2Repo-Bench | — | — | — |
| DeepSWE | — | — | — |
| WebDev Arena | — | — | — |
| Terminal-Bench 4.0 | — | — | — |
| CursorBench 4.0 | — | — | — |
| FrontierCode v1.1 (Main) | — | — | — |
| Arena | |||
| LMArena Elo | — | — | — |
Qwen3.6 27B
- Context
- 262K / 82K out
- Parameters
- 27B
- Price
- —
- Speed
- —
- Modalities
- text, image, video → text
- License
- Apache 2.0
Alibaba's first open-weight Qwen3.6 release — dense 27B model with native 262K context and multimodal (text/image/video) input, Apache 2.0.
Qwen3.7 Flash
- Context
- 1M / 66K out
- Parameters
- —
- Price
- $0.03 / $0.13
- Speed
- 140 tok/s · 0.25s TTFT
- Modalities
- text, image, video → text
- License
- Proprietary
Cheapest Qwen3.7 vision-language tier (Jul 2026) for high-volume multimodal agents; under-32K prompts at $0.03/$0.13 — no public quality scorecard yet.
Benchmark charts
Winner bars are emphasized. Per-benchmark deltas sit above each chart.
These two models have no overlapping published benchmarks in our dataset. Compare specs and pricing instead.