Model Comparison

Qwen3.6-27B vs Qwen3.5-122B-A10BWhich is better in 2026?

Qwen3.6-27B has a slight edge in benchmark performance. Qwen3.5-122B-A10B is 1.2x cheaper per token.

Verdict: Qwen3.6-27B vs Qwen3.5-122B-A10B — which is better?

Qwen3.6-27B (by Alibaba Cloud / Qwen Team) and Qwen3.5-122B-A10B (by Alibaba Cloud / Qwen Team) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.

Qwen3.6-27B outperforms in 17 benchmarks (CharXiv-R, CountBench, EmbSpatialBench, ERQA, GDPval-AA, GPQA, HMMT 2025, HMMT25, LiveCodeBench v6, RefCOCO-avg, RefSpatialBench, SWE-Bench Verified, Terminal-Bench 2.0, V*, VideoMME w sub., VideoMMMU, VLMsAreBlind), while Qwen3.5-122B-A10B is better at 16 benchmarks (CC-OCR, C-Eval, DynaMath, Humanity's Last Exam, MLVU, MMBench-V1.1, MMLU-Pro, MMLU-Redux, MMMU, MMMU-Pro, MMStar, MVBench, OCRBench, RealWorldQA, SimpleVQA, SuperGPQA). Qwen3.6-27B has a slight edge in benchmark performance.

On price, Qwen3.5-122B-A10B is roughly 1.2x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.

Choose Qwen3.6-27B if…

  • you want the most recent training data — it shipped Apr 2026

Choose Qwen3.5-122B-A10B if…

  • cost matters — it's about 1.2x cheaper per token

Performance Benchmarks

Comparative analysis across standard metrics

34 benchmarks

Qwen3.6-27B outperforms in 17 benchmarks (CharXiv-R, CountBench, EmbSpatialBench, ERQA, GDPval-AA, GPQA, HMMT 2025, HMMT25, LiveCodeBench v6, RefCOCO-avg, RefSpatialBench, SWE-Bench Verified, Terminal-Bench 2.0, V*, VideoMME w sub., VideoMMMU, VLMsAreBlind), while Qwen3.5-122B-A10B is better at 16 benchmarks (CC-OCR, C-Eval, DynaMath, Humanity's Last Exam, MLVU, MMBench-V1.1, MMLU-Pro, MMLU-Redux, MMMU, MMMU-Pro, MMStar, MVBench, OCRBench, RealWorldQA, SimpleVQA, SuperGPQA).

Qwen3.6-27B has a slight edge in benchmark performance.

Thu Jul 09 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Qwen3.5-122B-A10B costs less

For input processing, Qwen3.6-27B ($0.60/1M tokens) is 1.5x more expensive than Qwen3.5-122B-A10B ($0.40/1M tokens).

For output processing, Qwen3.6-27B ($3.60/1M tokens) is 1.1x more expensive than Qwen3.5-122B-A10B ($3.20/1M tokens).

In conclusion, Qwen3.6-27B is more expensive than Qwen3.5-122B-A10B.*

* Using a 3:1 ratio of input to output tokens

Lowest available price from all providers
Thu Jul 09 2026 • llm-stats.com
Alibaba Cloud / Qwen Team
Qwen3.6-27B
Input tokens$0.60
Output tokens$3.60
Best providerNovita
Alibaba Cloud / Qwen Team
Qwen3.5-122B-A10B
Input tokens$0.40
Output tokens$3.20
Best providerNovita
Notice missing or incorrect data?Start an Issue

Model Size

Parameter count comparison

94.2B diff

Qwen3.5-122B-A10B has 94.2B more parameters than Qwen3.6-27B, making it 339.1% larger.

Alibaba Cloud / Qwen Team
Qwen3.6-27B
27.8Bparameters
Alibaba Cloud / Qwen Team
Qwen3.5-122B-A10B
122.0Bparameters
27.8B
Qwen3.6-27B
122.0B
Qwen3.5-122B-A10B

Context Window

Maximum input and output token capacity

Both models have the same input context window of 262,144 tokens. Qwen3.6-27B can generate longer responses up to 65,536 tokens, while Qwen3.5-122B-A10B is limited to 64,000 tokens.

Alibaba Cloud / Qwen Team
Qwen3.6-27B
Input262,144 tokens
Output65,536 tokens
Alibaba Cloud / Qwen Team
Qwen3.5-122B-A10B
Input262,144 tokens
Output64,000 tokens
Thu Jul 09 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Both Qwen3.6-27B and Qwen3.5-122B-A10B support multimodal inputs.

They are both capable of processing various types of data, offering versatility in application.

Qwen3.6-27B

Text
Images
Audio
Video

Qwen3.5-122B-A10B

Text
Images
Audio
Video

License

Usage and distribution terms

Both models are licensed under Apache 2.0.

Both models share the same licensing terms, providing consistent usage rights.

Qwen3.6-27B

Apache 2.0

Open weights

Qwen3.5-122B-A10B

Apache 2.0

Open weights

Release Timeline

When each model was launched

Qwen3.6-27B was released on 2026-04-21, while Qwen3.5-122B-A10B was released on 2026-02-24.

Qwen3.6-27B is 2 months newer than Qwen3.5-122B-A10B.

Qwen3.6-27B

Apr 21, 2026

2 months ago

1mo newer
Qwen3.5-122B-A10B

Feb 24, 2026

4 months ago

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Provider Availability

Qwen3.6-27B is available from Novita. Qwen3.5-122B-A10B is available from Novita.

Qwen3.6-27B

novita logo
Novita
Input Price:Input: $0.60/1MOutput Price:Output: $3.60/1M

Qwen3.5-122B-A10B

novita logo
Novita
Input Price:Input: $0.40/1MOutput Price:Output: $3.20/1M
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion

Key Takeaways

Alibaba Cloud / Qwen Team

Qwen3.6-27B

View details

Alibaba Cloud / Qwen Team

Higher CharXiv-R score (78.4% vs 77.2%)
Higher CountBench score (97.8% vs 97.0%)
Higher EmbSpatialBench score (84.6% vs 83.9%)
Higher ERQA score (62.5% vs 62.0%)
Higher GDPval-AA score (38.6% vs 32.8%)
Higher GPQA score (87.8% vs 86.6%)
Higher HMMT 2025 score (93.8% vs 91.4%)
Higher HMMT25 score (90.7% vs 90.3%)
Higher LiveCodeBench v6 score (83.9% vs 78.9%)
Higher RefCOCO-avg score (92.5% vs 91.3%)
Higher RefSpatialBench score (70.0% vs 69.3%)
Higher SWE-Bench Verified score (77.2% vs 72.0%)
Higher Terminal-Bench 2.0 score (59.3% vs 49.4%)
Higher V* score (94.7% vs 93.2%)
Higher VideoMME w sub. score (87.7% vs 87.3%)
Higher VideoMMMU score (84.4% vs 82.0%)
Higher VLMsAreBlind score (97.0% vs 96.7%)
Alibaba Cloud / Qwen Team

Qwen3.5-122B-A10B

View details

Alibaba Cloud / Qwen Team

Less expensive input tokens
Less expensive output tokens
Higher CC-OCR score (81.8% vs 81.2%)
Higher C-Eval score (91.9% vs 91.4%)
Higher DynaMath score (85.9% vs 85.6%)
Higher Humanity's Last Exam score (47.5% vs 24.0%)
Higher MLVU score (87.3% vs 86.6%)
Higher MMBench-V1.1 score (92.8% vs 92.3%)
Higher MMLU-Pro score (86.7% vs 86.2%)
Higher MMLU-Redux score (94.0% vs 93.5%)
Higher MMMU score (83.9% vs 82.9%)
Higher MMMU-Pro score (76.9% vs 75.8%)
Higher MMStar score (82.9% vs 81.4%)
Higher MVBench score (76.6% vs 75.5%)
Higher OCRBench score (92.1% vs 89.4%)
Higher RealWorldQA score (85.1% vs 84.1%)
Higher SimpleVQA score (61.7% vs 56.1%)
Higher SuperGPQA score (67.1% vs 66.0%)

Detailed Comparison

Interactive Arena

Judge for yourself.

Run your own prompts against Qwen3.6-27B and Qwen3.5-122B-A10B side-by-side, then vote on the output you prefer.

Qwen3.6-27B
✓ Preferred
Qwen3.5-122B-A10B
Open in Playground
AI Model Comparison Table
Feature
Alibaba Cloud / Qwen Team
Qwen3.6-27B
Alibaba Cloud / Qwen Team
Qwen3.5-122B-A10B

FAQ

Common questions about Qwen3.6-27B vs Qwen3.5-122B-A10B.

Which is better, Qwen3.6-27B or Qwen3.5-122B-A10B?

Qwen3.6-27B has a slight edge in benchmark performance. Qwen3.6-27B is made by Alibaba Cloud / Qwen Team and Qwen3.5-122B-A10B is made by Alibaba Cloud / Qwen Team. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does Qwen3.6-27B compare to Qwen3.5-122B-A10B in benchmarks?

Qwen3.6-27B scores CountBench: 97.8%, VLMsAreBlind: 97.0%, V*: 94.7%, AIME 2026: 94.1%, HMMT 2025: 93.8%. Qwen3.5-122B-A10B scores CountBench: 97.0%, VLMsAreBlind: 96.7%, MMLU-Redux: 94.0%, IFEval: 93.4%, AI2D: 93.3%.

Is Qwen3.6-27B cheaper than Qwen3.5-122B-A10B?

Qwen3.5-122B-A10B is 1.5x cheaper for input tokens. Qwen3.6-27B costs $0.60/M input and $3.60/M output via novita. Qwen3.5-122B-A10B costs $0.40/M input and $3.20/M output via novita.

What are the context window sizes for Qwen3.6-27B and Qwen3.5-122B-A10B?

Qwen3.6-27B supports 262K tokens and Qwen3.5-122B-A10B supports 262K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between Qwen3.6-27B and Qwen3.5-122B-A10B?

Key differences include input pricing ($0.60 vs $0.40/M). See the full comparison above for benchmark-by-benchmark results.