vs
2:0
overall capability wins (0 ties across 2 scored). Qwen3.7 Flash leads overall.
- Specs2:0
Verdict
Qwen3.7 Flash offers ~15.3x larger context (1M vs 66K); Qwen3.7 Flash allows ~4.0x more max output (66K vs 16K); Qwen3-Omni-30B-A3B-Instruct ships open weights (Apache 2.0).
- Qwen3.7 Flash offers ~15.3x larger context (1M vs 66K)
- Qwen3.7 Flash allows ~4.0x more max output (66K vs 16K)
- Qwen3-Omni-30B-A3B-Instruct ships open weights (Apache 2.0)
Cost for 1M in + 250K out
Illustrative chat workload at primary-provider list prices.
- Qwen3.7 Flash
- $0.06
- Qwen3-Omni-30B-A3B-Instruct
- -
Price per 1M tokens
Full comparison
Organization
Alibaba
Alibaba
Family
Qwen
Qwen
License
Proprietary
Apache 2.0
Open weights
No
Yes
Release
Jul 27, 2026
Sep 22, 2025
Knowledge cutoff
-
-
API / provider
Alibaba Cloud
No primary API price listed
Modalities
text, image, video → text
text, image, audio, video → text, audio
Specs
Context window
1M
66K
Max output
66K
16K
Parameters
—
30B (3B act.)
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
—
62.0
Humanity's Last Exam
—
4.6
AIME 2025
—
—
MATH-500
—
—
Humanity's Last Exam (with tools)
—
—
AA-Omniscience Accuracy
—
14.3
AA-LCR v1.1
—
—
CritPt
—
—
MMMU-Pro
—
55.5
IFBench
—
31.2
Chartography
—
—
Chartography (With Tools)
—
—
Coding
SWE-bench Verified
—
—
SWE-bench Pro
—
—
SWE-bench Multilingual
—
—
LiveCodeBench
—
—
Terminal-Bench 2.1
—
—
Aider Polyglot
—
—
Terminal-Bench 3
—
—
BigCodeBench
—
—
SciCode
—
—
CursorBench
—
—
SWE-Rebench
—
—
NL2Repo-Bench
—
—
DeepSWE
—
—
WebDev Arena
—
—
Terminal-Bench 4.0
—
—
CursorBench 4.0
—
—
FrontierCode v1.1 (Main)
—
—
Arena
LMArena Elo
—
—
| Metric | Qwen3.7 Flash | Qwen3-Omni-30B-A3B-Instruct | Delta |
|---|---|---|---|
| Identity | |||
| Organization | Alibaba | Alibaba | — |
| Family | Qwen | Qwen | — |
| License | Proprietary | Apache 2.0 | — |
| Open weights | No | Yes | — |
| Release | Jul 27, 2026 | Sep 22, 2025 | — |
| Knowledge cutoff | - | - | — |
| API / provider | Alibaba Cloud | No primary API price listed | — |
| Modalities | text, image, video → text | text, image, audio, video → text, audio | — |
| Specs | |||
| Context window | 1M | 66K | A +934K |
| Max output | 66K | 16K | A +49K |
| Parameters | — | 30B (3B act.) | — |
| 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 | — | 62.0 | — |
| Humanity's Last Exam | — | 4.6 | — |
| AIME 2025 | — | — | — |
| MATH-500 | — | — | — |
| Humanity's Last Exam (with tools) | — | — | — |
| AA-Omniscience Accuracy | — | 14.3 | — |
| AA-LCR v1.1 | — | — | — |
| CritPt | — | — | — |
| MMMU-Pro | — | 55.5 | — |
| IFBench | — | 31.2 | — |
| Chartography | — | — | — |
| Chartography (With Tools) | — | — | — |
| Coding | |||
| SWE-bench Verified | — | — | — |
| SWE-bench Pro | — | — | — |
| SWE-bench Multilingual | — | — | — |
| LiveCodeBench | — | — | — |
| Terminal-Bench 2.1 | — | — | — |
| Aider Polyglot | — | — | — |
| Terminal-Bench 3 | — | — | — |
| BigCodeBench | — | — | — |
| SciCode | — | — | — |
| CursorBench | — | — | — |
| SWE-Rebench | — | — | — |
| NL2Repo-Bench | — | — | — |
| DeepSWE | — | — | — |
| WebDev Arena | — | — | — |
| Terminal-Bench 4.0 | — | — | — |
| CursorBench 4.0 | — | — | — |
| FrontierCode v1.1 (Main) | — | — | — |
| Arena | |||
| LMArena Elo | — | — | — |
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.
Qwen3-Omni-30B-A3B-Instruct
- Context
- 66K / 16K out
- Parameters
- 30B (3B act.)
- Price
- —
- Speed
- —
- Modalities
- text, image, audio, video → text, audio
- License
- Apache 2.0
Alibaba's first natively end-to-end omni-modal model (text/image/audio/video in; text and streamed speech out) via a Thinker-Talker MoE architecture; DashScope hosting pricing not publicly listed.
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.