How does Qwen3.6 27B rank among AI models?
Qwen3.6 27B is a superseded version and is not on the current board as of 6 November 2026. Its archive position, where one exists, is shown on the page; the newer version carries the current rank.
Qwen·#148 of 504 in the all-versions archive
Qwen3.6 27B is a language model from Qwen. It ranks #148 of 504 in ModelCap's all-versions archive, with an Index score of 58.9, modeled from its measured predecessor. Its score range of 48.3–69.5 spans positions #91–#227. It is listed at $0.45 per million input tokens and $2.70 per million output tokens across 7 providers. It reads up to 262K tokens of context, and its weights are openly downloadable.
Figures as of 6 Nov 2026, 20:32 UTC · download the public dataset · methodology
ModelCap Index · All-versions archive
58.9#148
Modeled from its measured predecessor · range 48.3–69.5
Superseded by Qwen3.8 27B. It no longer holds a current-board position; its all-versions archive rank is #148.
Input
$0.45
per 1M tokens
Output
$2.70
per 1M tokens
Context
262K
tokens
Providers
7
endpoints
Providers
Uptime measured over the last 30 minutesHugging Face Inference Providers
Exact repository identityProvider economics and telemetry published by the Hugging Face Router. These are operational signals, not benchmark results.
Reported evaluation leads (20)
Not independent measurementStructured rows reported in this exact model repository at the pinned revision below. They are the publisher talking about itself: never measured evidence, never a score input on their own. Rows marked as used were laddered against independently measured peers inside the launch-card channel of this model's modeled placement, with the replay-measured optimism haircut applied; the others played no part.
Other versions (2)
ModelCap Index · All-versions archive
Methodology58.9
Modeled from its measured predecessor
All-versions archive
#148 of 504
- Score range
- 48.3–69.5
- Rank range
- #91–#227
Technical details
- Public rank basis
- Modeled · succession (architecture)
- Evidence label
- Modeled · succession · publisher-corpus-prior over 210 held-out anchors (12%); opens above measured predecessor qwen/qwen3.5-27b on same-family ordering evidence (48%); launch card against 4 resolved peers on 17 rows (41%); optimism haircut 0 from cross-lab-probe; shrunk 1.1 toward the measured corpus
- Index support
- 40.0% · limited
- Identity binding
- Exact catalogue product · qwen/qwen/qwen3.6-27b · aggregates disclosed benchmark configurations · endpoint configuration not claimed · artifact metadata Qwen/Qwen3.6-27B@6a9e13bd6fc8f0983b9b99948120bc37f49c13e9 (not the evaluation revision)
- Index observations used
- 0
- Qualified Index evidence point
- 58.9
How this position was produced · fused launch placement
No general-family board lists this model yet. Its point estimate is the precision-weighted mean of every channel that spoke for it; each channel’s share is the weight it carried. Independent measurements replace this placement as boards list the model.
- Fused estimate
- 58.9 ± 8.3
- Independent share
- 0%
- Shrink toward corpus
- -1.1
- Peer ceiling share
- 0%
- Optimism haircut
- None required · cross lab probe, 5 probes
- Method
- precision-weighted-v1
- · Launch card row aime-26: inside the resolved peer range
- · Launch card row erqa: inside the resolved peer range
- · Launch card row gpqa-diamond: inside the resolved peer range
- · Launch card row hle: inside the resolved peer range
- · Launch card row hmmt-2026: inside the resolved peer range
- · Launch card row hmmt-feb-25: inside the resolved peer range
- · Launch card row hmmt-nov-25: inside the resolved peer range
- · Launch card row livecodebench-v-6: inside the resolved peer range
- · Launch card row mmlu-pro: inside the resolved peer range
- · Launch card row mmlu-redux: inside the resolved peer range
- · Launch card row mmmu-pro: inside the resolved peer range
- · Launch card row mmmu: inside the resolved peer range
- · Launch card row nl-2-repo: inside the resolved peer range
- · Launch card row swe-bench-multilingual: inside the resolved peer range
- · Launch card row swe-bench-pro: inside the resolved peer range
- · Launch card row swe-bench-verified: inside the resolved peer range
- · Launch card row terminal-bench-2-0: inside the resolved peer range
Bayesian Evidence Score · measured evidence input only
BES normalizes admitted public benchmark evidence for measured Index rows. Its legacy score and observables prior do not define the public language rank.
- Observed capability
- —
- Evidence status
- No admitted measured evidence
- General preference
- —
- Coding
- —
- Agents & tools
- —
- Reasoning
- —
- Evidence breadth
- 0%
- Evidence coverage
- 0%
Market and catalogue signals · secondary, never the language Index rank
- Usage (OpenRouter popularity)
- 13.0
- Liquidity (providers × uptime)
- 99.5
- Open reach (HF downloads)
- 98.7
- Surface (context / tools / modalities)
- 90.3
- Freshness
- 47.8
- · OpenRouter popularity #159
- · 7 live providers
- · HF 30d downloads 2324676
- · Catalogue: context 262144, reasoning, tools, modalities Text/Image/Video
- · First seen 2026-04-27
These adoption and deployment observations remain context only. They do not change this model's ModelCap Index score or rank.
The bounded 0–100 capability score blends each source’s competitive placement with its published achievement, then combines capability families. Evidence breadth adds a modest uncertainty adjustment; price and popularity are not part of benchmark-led rank.
Experimental capability estimate
Estimate policy50.1
36.1–64.2 uncertainty interval
- Confidence
- 78%
- Training support
- 219 models · 128 lineages
- Out-of-domain check
- in domain
- Feature coverage
- 100%
- Lineage-held-out error
- 8.3 MAE
Experimental estimate. This layer predicts from admitted evidence and safe metadata only. It never enters ModelCap Score or rank.
Deployment readiness
Metadata index74.8
partial · 76% metadata coverage
- Artifact reproducibility
- 85.0
- Access & legal clarity
- 100.0
- Deployability
- 97.5
- Evaluation provenance
- 0.0
Not yet published: the base model it derives from, admitted benchmark results, results across several capability areas, results from more than one source, a confident match to its benchmark entries, pinned evaluation dataset versions.
A metadata completeness and deployability index—not a safety certification, quality grade, or production-readiness claim.
Market activity
Venue coverage- Market Gravity
- 49.2
- OpenRouter weekly popularity
- #159 of 339
Market Gravity uses OpenRouter’s full-catalogue popularity order. Sparse Vercel top-ten observations appear only when published and do not affect the score.
Arena preference
No matched Arena rating
This model is visible because it is available in the live catalogue, but it has no trustworthy community result yet. ModelCap does not infer a quality score from price, features, or market activity.
Market Gravity breakdown
- Usage55%
- 13.0
- Liquidity25%
- 99.5
- Open reach15%
- 98.7
- Freshness5%
- 47.8
OpenRouter weekly popularity across the full model catalogue.
Independent providers versus the model's open or closed cohort, weighted by uptime.
Hugging Face 30-day downloads for open models; neutral for closed models.
Time since first public availability, on a six-month half-life.
Specification
- Model ID
qwen/qwen3.6-27b- Context window
- 262K tokens
- Declared output cap
- 66K tokens
OpenRouter catalogue or provider-endpoint declaration. Limits can vary by provider and prompt length; a model-wide maximum has not been independently verified. OpenRouter metadata
- Inputs
- Text, Image, Video
- Outputs
- Text
- Tokenizer
- Qwen3
- Cached input tokens
- Not offered
- First seen on OpenRouter
- 27 Apr 2026
Capabilities
- Supported: Reasoning
- Supported: Tool use
- Supported: Structured output
- Supported: Response format
- Not supported: Moderated
Weights & access
Open weights. Downloadable weights are published under an OSI or free-culture license in the current repository metadata. This describes weight availability, not whether the full training stack qualifies as Open Source AI.
- Access
- Open weights
- Licence
- apache-2.0
- Pinned revision
- 6a9e13bd6fc8
- Parameters
- 27.8B
- Architecture
- Qwen3_5ForConditionalGeneration
- Model type
- qwen3_5
- Hugging Face downloads (30d)
- 2.3M
- Downloads (all time)
- 30.6M
- Likes
- 2K
- Repository
- Qwen/Qwen3.6-27B
- Repository updated
- 24 Apr 2026, 02:39 UTC
Benchmark leaderboards featuring Qwen3.6 27B
All leaderboardsQwen3.6 27B: common questions
How much does Qwen3.6 27B cost per 1M tokens?
Qwen3.6 27B is listed at $0.45 per 1M input tokens and $2.70 per 1M output tokens as of 6 November 2026; the lowest output price among 7 providers is $2.00 on Chutes. Prices come from the OpenRouter catalogue and re-observe on every refresh. For 1,000 requests using 2,000 input and 500 output tokens each, the listed rates imply $2.25 total (2M input + 0.5M output). This excludes caching, batch discounts, prompt-length tiers, tool charges and retries; verify the chosen endpoint's rate and limits before budgeting.
What is the context window of Qwen3.6 27B?
Qwen3.6 27B has a published context window of 262K tokens (262,144). The snapshot lists a 65,536 token catalogue-declared route output cap. OpenRouter catalogue or provider-endpoint declaration. Limits can vary by provider and prompt length; a model-wide maximum has not been independently verified. The providers table lists each endpoint's context limit.
Is Qwen3.6 27B open-weight?
Open weights (apache-2.0). Downloadable weights are published under an OSI or free-culture license in the current repository metadata. This describes weight availability, not whether the full training stack qualifies as Open Source AI. The repository is Qwen/Qwen3.6-27B on Hugging Face.
Which API providers serve Qwen3.6 27B?
7 providers serve Qwen3.6 27B through OpenRouter as of 6 November 2026: Reka, Chutes, SiliconFlow, DeepInfra, Phala and Venice and 1 more. Each provider's price, context limit, quantization and measured uptime are in the providers table above.
Which benchmarks has Qwen3.6 27B been evaluated on?
Qwen3.6 27B has published results on 1 public board tracked by ModelCap as of 6 November 2026: WildClawBench OpenClaw 43.2 (#20 of 34). Each board page ranks every tracked model on that benchmark. The ModelCap Index scores the results its methodology admits, with published uncertainty, and shows reference boards without scoring them.
When was Qwen3.6 27B released?
Qwen3.6 27B first appeared in the catalogue on 27 Apr 2026. It has since been superseded by Qwen3.8 27B. Rank and price movements since then are recorded on the site's changes feed.