How does GLM 4.7 rank among AI models?
GLM 4.7 is a superseded version and is not on the current board as of 22 September 2026. Its archive position, where one exists, is shown on the page; the newer version carries the current rank.
Z.ai·z-ai/glm-4.7
GLM 4.7 is ModelCap archive rank #71 (Measured), from the ModelCap Index as of 22 Sept 2026, 02:49 UTC.
GLM 4.7 listed OpenRouter input price is $0.40 per 1M tokens and listed output price is $1.75 per 1M tokens, from OpenRouter listed prices as of 22 Sept 2026, 02:49 UTC.
GLM 4.7 has a published context limit of 205K tokens in the ModelCap catalogue as of 22 Sept 2026, 02:49 UTC.
GLM 4.7 has 7 published OpenRouter serving endpoints, from OpenRouter as of 22 Sept 2026, 02:49 UTC.
GLM 4.7 weight access is open weights, from ModelCap classification of published repository metadata as of 22 Sept 2026, 02:49 UTC.
Snapshot facts · download the public dataset · methodology
ModelCap Index · All-versions archive
69.2#71
Measured · 62% support · score interval 64.1–74.3
Evidence as of 13 Sept 2026, 00:00 UTC
Superseded by GLM 5.3. It no longer holds a current-board position; its all-versions archive rank is #71.
Input
$0.40
per 1M tokens
Output
$1.75
per 1M tokens
Context
205K
tokens
Providers
7
endpoints
Gateway spend
—
latest · Vercel share
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 (9)
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. They remain quarantined until dataset revision, harness, configuration and identity pass ModelCap’s independent admission review.
Other versions (6)
ModelCap Index · All-versions archive
Methodology69.2
Measured · 2 public benchmark observations across 2 boards
All-versions archive
#71
- Public rank basis
- Measured (measured)
- Index support
- 61.8% · strong
- Index interval
- 64.1–74.3
- Rank posterior
- Not available
- Top 5 / Top 10 probability
- Not available / Not available
- Posterior as of
- Snapshot timestamp unavailable
- Evidence as of
- 13 Sept 2026, 00:00 UTC
- Identity binding
- Exact catalogue product · z-ai/z-ai/glm-4.7 · aggregates disclosed benchmark configurations · endpoint configuration not claimed · artifact metadata zai-org/GLM-4.7@602d01efcdd332c5238ca4bcede555defbe83eb7 (not the evaluation revision)
- Index observations used
- 2
- Qualified Index evidence point
- 69.2
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
- 69.2
- Evidence status
- confirmed · 77% mass
- General preference
- 69.6
- Coding
- 65.8
- Agents & tools
- —
- Reasoning
- —
- Evidence breadth
- 92%
- Evidence coverage
- 62%
Market and catalogue signals · secondary, never the language Index rank
- Usage (OpenRouter popularity)
- 20.8
- Liquidity (providers × uptime)
- 98.9
- Open reach (HF downloads)
- 88.6
- Surface (context / tools / modalities)
- 73.5
- Freshness
- 35.2
- · OpenRouter popularity #97
- · 7 live providers
- · HF 30d downloads 102706
- · Catalogue: context 204800, reasoning, tools
- · First seen 2025-12-22
These adoption and deployment observations remain context only. They do not change this model's ModelCap Index score or rank.
BES evidence selected · measured input only
- Arena Overall69.6
- Arena Coding65.8
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.
Deployment readiness
Metadata index85.2
strong · 94% metadata coverage
- Artifact reproducibility
- 85.0
- Access & legal clarity
- 100.0
- Deployability
- 98.0
- Evaluation provenance
- 51.6
Missing: artifactReproducibility.base-lineage, evaluationProvenance.evaluation-dataset-revisions.
A metadata completeness and deployability index—not a safety certification, quality grade, or production-readiness claim.
Market activity
Venue coverage- Market Gravity
- 51.2
- OpenRouter weekly popularity
- #97 of 322
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
- Status
- Ranked
- Source
- LMArena
- Official rank
- #83 of 402
- Rating
- 1441.7
- 95% confidence interval
- 1436–1448
- Votes
- 12K
- Source published at
- 13 Sept 2026, 00:00 UTC
- Category
- overall
- Identity match confidence
- 100%
Market Gravity breakdown
- Usage55%
- 20.8
- Liquidity25%
- 98.9
- Open reach15%
- 88.6
- Freshness5%
- 35.2
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
- Context window
- 205K tokens
- Max output
- 131K tokens
- Inputs
- Text
- Outputs
- Text
- Tokenizer
- Other
- Cached input tokens
- $0.08 / 1M
- First seen on OpenRouter
- 22 Dec 2025
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
- mit
- Pinned revision
- 602d01efcdd3
- Parameters
- 358.3B
- Architecture
- Glm4MoeForCausalLM
- Model type
- glm4_moe
- Hugging Face downloads (30d)
- 103K
- Downloads (all time)
- 822K
- Likes
- 2K
- Repository
- zai-org/GLM-4.7
- Repository updated
- 29 Jan 2026, 08:05 UTC
Benchmark leaderboards featuring GLM 4.7
All leaderboardsGLM 4.7: common questions
How much does GLM 4.7 cost per 1M tokens?
GLM 4.7 is listed at $0.40 per 1M input tokens and $1.75 per 1M output tokens as of 22 September 2026 across 7 providers. 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 $1.67 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 GLM 4.7?
GLM 4.7 has a published context window of 205K tokens (204,800) and a maximum output of 131K tokens. Individual providers can serve less than the published maximum; the providers table lists each endpoint's own limit.
Is GLM 4.7 open-weight?
Open weights (mit). 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 zai-org/GLM-4.7 on Hugging Face.
Which API providers serve GLM 4.7?
7 providers serve GLM 4.7 through OpenRouter as of 22 September 2026: DeepInfra, Venice, AtlasCloud, Novita, Google and Z.AI and 1 more. Each provider's price, context limit, quantization and measured uptime are in the providers table above.
Which benchmarks has GLM 4.7 been evaluated on?
GLM 4.7 has published results on 3 public boards tracked by ModelCap as of 22 September 2026: Arena coding 1485 (#91 of 397), AA τ²-Bench Telecom 95.9% (#16 of 436) and AA Humanity's Last Exam 27.4% (#140 of 615). Each board page ranks every tracked model on that benchmark; the ModelCap Index combines them with published uncertainty.
When was GLM 4.7 released?
GLM 4.7 first appeared in the catalogue on 22 Dec 2025. It has since been superseded by GLM 5.3. Rank and price movements since then are recorded on the site's changes feed.