Model Comparison

Qwen3.6-27B vs Qwen3.6 PlusWhich is better in 2026?

Qwen3.6 Plus significantly outperforms across most benchmarks. Qwen3.6 Plus is 1.2x cheaper per token.

Verdict: Qwen3.6-27B vs Qwen3.6 Plus — which is better?

Qwen3.6-27B (by Alibaba Cloud / Qwen Team) and Qwen3.6 Plus (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 4 benchmarks (Claw-Eval, CountBench, SkillsBench, VideoMMMU), while Qwen3.6 Plus is better at 30 benchmarks (AIME 2026, CC-OCR, C-Eval, CharXiv-R, DynaMath, ERQA, GDPval-AA, GPQA, HMMT 2025, HMMT25, HMMT Feb 26, Humanity's Last Exam, IMO-AnswerBench, LiveCodeBench v6, MLVU, MMLU-Pro, MMLU-Redux, MMMU, MMMU-Pro, MMStar, NL2Repo, RealWorldQA, RefCOCO-avg, SimpleVQA, SuperGPQA, SWE-bench Multilingual, SWE-Bench Pro, SWE-Bench Verified, Terminal-Bench 2.0, V*). Qwen3.6 Plus significantly outperforms across most benchmarks.

On price, Qwen3.6 Plus is roughly 1.2x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.

Qwen3.6 Plus also accepts a larger context window (1,000,000 input tokens), making it the stronger choice for long documents and large codebases.

Choose Qwen3.6-27B if…

  • you want the most recent training data — it shipped Apr 2026
  • you need open weights you can self-host or fine-tune

Choose Qwen3.6 Plus if…

  • you want the strongest raw capability — it leads on 30 of 34 shared benchmarks
  • cost matters — it's about 1.2x cheaper per token
  • you process long inputs — it offers a 1,000,000 token context window

Performance Benchmarks

Comparative analysis across standard metrics

34 benchmarks

Qwen3.6-27B outperforms in 4 benchmarks (Claw-Eval, CountBench, SkillsBench, VideoMMMU), while Qwen3.6 Plus is better at 30 benchmarks (AIME 2026, CC-OCR, C-Eval, CharXiv-R, DynaMath, ERQA, GDPval-AA, GPQA, HMMT 2025, HMMT25, HMMT Feb 26, Humanity's Last Exam, IMO-AnswerBench, LiveCodeBench v6, MLVU, MMLU-Pro, MMLU-Redux, MMMU, MMMU-Pro, MMStar, NL2Repo, RealWorldQA, RefCOCO-avg, SimpleVQA, SuperGPQA, SWE-bench Multilingual, SWE-Bench Pro, SWE-Bench Verified, Terminal-Bench 2.0, V*).

Qwen3.6 Plus significantly outperforms across most benchmarks.

Fri Jul 17 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Qwen3.6 Plus costs less

For input processing, Qwen3.6-27B ($0.60/1M tokens) is 1.2x more expensive than Qwen3.6 Plus ($0.50/1M tokens).

For output processing, Qwen3.6-27B ($3.60/1M tokens) is 1.2x more expensive than Qwen3.6 Plus ($3.00/1M tokens).

In conclusion, Qwen3.6-27B is more expensive than Qwen3.6 Plus.*

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

Lowest available price from all providers
Fri Jul 17 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
Qwen3.6 Plus
Input tokens$0.50
Output tokens$3.00
Best providerTogether
Notice missing or incorrect data?Start an Issue→

Context Window

Maximum input and output token capacity

Qwen3.6 Plus accepts 1,000,000 input tokens compared to Qwen3.6-27B's 262,144 tokens. Both models can generate responses up to 65,536 tokens.

Alibaba Cloud / Qwen Team
Qwen3.6-27B
Input262,144 tokens
Output65,536 tokens
Alibaba Cloud / Qwen Team
Qwen3.6 Plus
Input1,000,000 tokens
Output65,536 tokens
Fri Jul 17 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Both Qwen3.6-27B and Qwen3.6 Plus support multimodal inputs.

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

Qwen3.6-27B

Text
Images
Audio
Video

Qwen3.6 Plus

Text
Images
Audio
Video

License

Usage and distribution terms

Qwen3.6-27B is licensed under Apache 2.0, while Qwen3.6 Plus uses a proprietary license.

License differences may affect how you can use these models in commercial or open-source projects.

Qwen3.6-27B

Apache 2.0

Open weights

Qwen3.6 Plus

Proprietary

Closed source

Release Timeline

When each model was launched

Qwen3.6-27B was released on 2026-04-21, while Qwen3.6 Plus was released on 2026-03-31.

Qwen3.6-27B is 1 month newer than Qwen3.6 Plus.

Qwen3.6-27B

Apr 21, 2026

2 months ago

3w newer
Qwen3.6 Plus

Mar 31, 2026

3 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. Qwen3.6 Plus is available from Together.

Qwen3.6-27B

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

Qwen3.6 Plus

together logo
Together
Input Price:Input: $0.50/1MOutput Price:Output: $3.00/1M
* 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

Has open weights
Higher Claw-Eval score (60.6% vs 58.7%)
Higher CountBench score (97.8% vs 97.6%)
Higher SkillsBench score (48.2% vs 45.7%)
Higher VideoMMMU score (84.4% vs 84.0%)
Alibaba Cloud / Qwen Team

Qwen3.6 Plus

View details

Alibaba Cloud / Qwen Team

Larger context window (1,000,000 tokens)
Less expensive input tokens
Less expensive output tokens
Higher AIME 2026 score (95.3% vs 94.1%)
Higher CC-OCR score (83.4% vs 81.2%)
Higher C-Eval score (93.3% vs 91.4%)
Higher CharXiv-R score (81.5% vs 78.4%)
Higher DynaMath score (88.0% vs 85.6%)
Higher ERQA score (65.7% vs 62.5%)
Higher GDPval-AA score (38.7% vs 38.6%)
Higher GPQA score (90.4% vs 87.8%)
Higher HMMT 2025 score (96.7% vs 93.8%)
Higher HMMT25 score (94.6% vs 90.7%)
Higher HMMT Feb 26 score (87.8% vs 84.3%)
Higher Humanity's Last Exam score (28.8% vs 24.0%)
Higher IMO-AnswerBench score (83.8% vs 80.8%)
Higher LiveCodeBench v6 score (87.1% vs 83.9%)
Higher MLVU score (86.7% vs 86.6%)
Higher MMLU-Pro score (88.5% vs 86.2%)
Higher MMLU-Redux score (94.5% vs 93.5%)
Higher MMMU score (86.0% vs 82.9%)
Higher MMMU-Pro score (78.8% vs 75.8%)
Higher MMStar score (83.3% vs 81.4%)
Higher NL2Repo score (37.9% vs 36.2%)
Higher RealWorldQA score (85.4% vs 84.1%)
Higher RefCOCO-avg score (93.5% vs 92.5%)
Higher SimpleVQA score (67.3% vs 56.1%)
Higher SuperGPQA score (71.6% vs 66.0%)
Higher SWE-bench Multilingual score (73.8% vs 71.3%)
Higher SWE-Bench Pro score (56.6% vs 53.5%)
Higher SWE-Bench Verified score (78.8% vs 77.2%)
Higher Terminal-Bench 2.0 score (61.6% vs 59.3%)
Higher V* score (96.9% vs 94.7%)

Detailed Comparison

Interactive Arena

Judge for yourself.

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

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

FAQ

Common questions about Qwen3.6-27B vs Qwen3.6 Plus.

Which is better, Qwen3.6-27B or Qwen3.6 Plus?

Qwen3.6 Plus significantly outperforms across most benchmarks. Qwen3.6-27B is made by Alibaba Cloud / Qwen Team and Qwen3.6 Plus 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 Qwen3.6 Plus in benchmarks?

Qwen3.6-27B scores CountBench: 97.8%, VLMsAreBlind: 97.0%, V*: 94.7%, AIME 2026: 94.1%, HMMT 2025: 93.8%. Qwen3.6 Plus scores CountBench: 97.6%, V*: 96.9%, HMMT 2025: 96.7%, AIME 2026: 95.3%, HMMT25: 94.6%.

Is Qwen3.6-27B cheaper than Qwen3.6 Plus?

Qwen3.6 Plus is 1.2x cheaper for input tokens. Qwen3.6-27B costs $0.60/M input and $3.60/M output via novita. Qwen3.6 Plus costs $0.50/M input and $3.00/M output via together.

What are the context window sizes for Qwen3.6-27B and Qwen3.6 Plus?

Qwen3.6-27B supports 262K tokens and Qwen3.6 Plus supports 1.0M 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 Qwen3.6 Plus?

Key differences include context window (262K vs 1.0M), input pricing ($0.60 vs $0.50/M), licensing (Apache 2.0 vs Proprietary). See the full comparison above for benchmark-by-benchmark results.

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