Qwen3 4B Instruct 2507 is ModelCap current-board rank #151 with Index 35.4 (Modeled · peer benchmarks), from the ModelCap Index as of 22 Sept 2026, 03:10 UTC.
Qwen3 4B Instruct 2507 has a published context limit of 262K tokens in the ModelCap catalogue as of 22 Sept 2026, 03:10 UTC.
Qwen3 4B Instruct 2507 weight access is open weights, from ModelCap classification of published repository metadata as of 22 Sept 2026, 03:10 UTC.
OpenRouter endpoint status could not be refreshed. This model is not treated as currently served until a successful observation arrives.
Input
Self-hosted
weights only · no listed API price
Output
Self-hosted
weights only · no listed API price
Context
262K
tokens
Providers
0
endpoints
Gateway spend
—
latest · Vercel share
Providers
Uptime measured over the last 30 minutes
Current provider status is unavailable.
Reported evaluation leads (2)
Not independent measurement
Structured 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.
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
35.4 ± 9.7
Independent share
0%
Shrink toward corpus
+4.0
Peer ceiling share
78%
Optimism haircut
None applied
Method
precision-weighted-v1
Channels fused into this model’s launch placement
Channel
Estimate
Share
Publisher corpus prior · not independent
publisher-corpus-prior over 203 held-out anchors
53.5 ± 22.7
18%
Launch card against resolved peers · not independent
launch card against 2 resolved peers on 9 rows
31.4 ± 10.7
82%
· Launch card row aime-25: claims above every resolved peer
· Launch card row bfcl-v-3: claims above every resolved peer
· Launch card row gpqa: claims above every resolved peer
· Launch card row hmmt-25: claims above every resolved peer
· Launch card row ifeval: inside the resolved peer range
· Launch card row livecodebench-v-6: claims above every resolved peer
· Launch card row mmlu-pro: claims above every resolved peer
· Launch card row mmlu-prox: inside the resolved peer range
· Launch card row mmlu-redux: claims above every resolved peer
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)
—
Liquidity (providers × uptime)
—
Open reach (HF downloads)
99.0
Surface (context / tools / modalities)
30.3
Freshness
20.8
· HF 30d downloads 3982875
· Catalogue: context 262144
· First seen 2025-08-05
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.
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%
0.0
OpenRouter weekly popularity across the full model catalogue.
Liquidity25%
0.0
Independent providers versus the model's open or closed cohort, weighted by uptime.
Open reach15%
99.0
Hugging Face 30-day downloads for open models; neutral for closed models.
Freshness5%
20.8
Time since first public availability, on a six-month half-life.
Specification
Context window
262K tokens
Max output
—
Inputs
Text
Outputs
Text
Tokenizer
—
Cached input tokens
Not offered
First seen on OpenRouter
5 Aug 2025
Capabilities
Not supported: Reasoning
Not supported: Tool use
Not supported: Structured output
Not 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.
How does Qwen3 4B Instruct 2507 rank among AI models?
Qwen3 4B Instruct 2507 holds ModelCap Index position #151 of 248 ranked language models as of 22 September 2026, with an Index score of 35.4 (interval 23.0–47.8); evidence: modeled · peer benchmarks. The Index combines public benchmark boards with published uncertainty; the methodology page explains the weighting.
How much does Qwen3 4B Instruct 2507 cost per 1M tokens?
No live API price is listed for Qwen3 4B Instruct 2507 in the current snapshot as of 22 September 2026 because the latest endpoint refresh was unavailable. ModelCap shows a price only when a provider currently lists one.
What is the context window of Qwen3 4B Instruct 2507?
Qwen3 4B Instruct 2507 has a published context window of 262K tokens (262,144). Individual providers can serve less than the published maximum; the providers table lists each endpoint's own limit.
Is Qwen3 4B Instruct 2507 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/Qwen3-4B-Instruct-2507 on Hugging Face.
Which benchmarks has Qwen3 4B Instruct 2507 been evaluated on?
Qwen3 4B Instruct 2507 has published results on 1 public board tracked by ModelCap as of 22 September 2026: BFCL V4 35.7 (#40 of 83). Each board page ranks every tracked model on that benchmark; the ModelCap Index combines them with published uncertainty.
How does Qwen3 4B Instruct 2507 compare with granite 4.2 30b?
Qwen3 4B Instruct 2507 ranks #151 (Index 35.4) and granite 4.2 30b ranks #150 (Index 36.2) on the ModelCap Index as of 22 September 2026. The head-to-head page lines up their benchmarks, listed prices, context windows and provider counts side by side.
When was Qwen3 4B Instruct 2507 released?
Qwen3 4B Instruct 2507 first appeared in the catalogue on 5 Aug 2025. It is the current version in its family. Rank and price movements since then are recorded on the site's changes feed.