GLM-4.7
Price from the model maker's own API, verified 8h ago · cached input $0.11 /M
Benchmarks
preference
Crowdsourced pairwise human preference rankings of LLM responses. Higher Elo means more frequently preferred by users.
performance
Median output speed in tokens per second over the last 14 days on the model maker's own API, measured nightly with a fixed ~800-token prompt and ~250-token answer. Models not yet probed fall back to a dated manual figure. Higher is faster.
Median time from request to first streamed token in milliseconds over the last 14 days on the model maker's own API, using the same nightly probe prompt. Lower is snappier; reasoning models are penalised here because they think before talking.
math
Mathematical research problems spanning analysis, algebra, combinatorics and number theory. Tiers 1-3 are progressively harder; even frontier reasoning models only solve a small fraction. The hardest publicly reported benchmark for general mathematical reasoning.
AIME-style competition problems written specifically for the OTIS mock contest, then run as an evaluation by Epoch AI. Closer in spirit to the public AIME but with novel problems unlikely to appear in training data.
knowledge
A human-validated factuality benchmark of short factual questions whose answers can be checked against a single ground truth. Penalises hallucinations by scoring confidently-wrong answers below abstentions.
agentic
Long-horizon shell-and-filesystem tasks executed in a sandboxed terminal, scored by whether the agent's final state matches a target state. Tests practical tool-using ability for everyday devops and data-wrangling work; one of the hardest agentic benchmarks today.
composite
Saturation-resistant composite capability score stitched together from ~40 underlying benchmarks using Item Response Theory. Each benchmark is weighted by its fitted difficulty and discriminative slope, so doing well on hard, contamination-resistant evals (FrontierMath, ARC-AGI 2, Humanity's Last Exam) moves the score and saturated benchmarks contribute almost nothing. Imported per-model from Epoch AI's published index; we anchor it to the same min-max scale we use for every other benchmark so it's directly weightable in scenarios.
Reliability monitor
Loading drift signal…
Hosted endpoints
| Host | Input $/M | Output $/M | Context | Speed | TTFT | Uptime 24h | Quant |
|---|---|---|---|---|---|---|---|
| Host 48 | $0.32 | $1.40 | 203k | — | — | — | fp4 |
| Host 45 | $0.40 | $1.75 | 203k | — | — | 100.0% | fp4 |
| Host 49 | $0.40 | $1.93 | 198k | — | — | 99.7% | fp4 |
| Host 47 | $0.54 | $1.98 | 205k | — | — | 100.0% | fp8 |
| Host 35 | $0.60 | $2.20 | 200k | — | — | 0.0% | unknown |
| Host 50maker's API | $0.60 | $2.20 | 203k | 134 tok/s | 9.95 s | 100.0% | fp4 |
| Host 51 | $0.70 | $2.50 | 131k | — | — | 99.2% | fp4 |