glm-4.7-flash: benchmark scores, price and ranking

glm-4.7-flash from zai-org ranks #215 of 743 LLMs on the LLMs Tiger aggregate benchmark score (50.1), based on 3 benchmark results captured up to 2026-10-13.

Organisation: zai-org
Type: Open-weight / local model
Open weights: Yes
Released: 2026-01-19 (source: openrouter)
Cheapest API price: $0.060 in / $0.400 out per million tokens (via deepinfra, observed 2026-10-13T20:47Z)
Aggregate rank: #215 of 743 (score 50.1)

Relative to other models, glm-4.7-flash is strongest on GPQA Diamond (#130 of 275) and weakest on Chess Puzzles (#105 of 122).

Across the 3 benchmarks where at least ten models are scored, glm-4.7-flash is in the top 10% on 0 and in the top half on 2.

Its cheapest listed input price, $0.060 per million tokens, is lower than that of 89% of the 427 models with a listed price.

glm-4.7-flash benchmark results

glm-4.7-flash benchmark scores, ranked against all models measured on each benchmark
Benchmark	Score	Rank	Source	Captured
GPQA Diamond	75.2%	#130 of 275	HuggingFace GPQA Diamond leaderboard	2026-10-13
OTIS Mock AIME	58.3%	#94 of 189	Epoch AI Benchmarking Hub	2026-10-13
Chess Puzzles	0.0%	#105 of 122	Epoch AI Benchmarking Hub	2026-10-13

About glm-4.7-flash

glm-4.7-flash is an open-weight model that can run on your own hardware from zai-org. It was released on 2026-01-19, according to the date OpenRouter first listed it (approximate). The cheapest API listing we track charges $0.060 per million input tokens and $0.400 per million output tokens, through deepinfra. We hold 3 benchmark results for it across 2 areas, and none is in the top tenth of its board.

Where glm-4.7-flash stands

On our aggregate score, glm-4.7-flash is #215 of 743 ranked models, around the middle of the table. Its score is 50.1; the scale is centred on 50, so it sits 0.1 points above the typical model. That beats 71% of the ranked field. Among the 6 ranked zai-org models it is #5. The next one up is GLM-4.6-FP8 at 51.0. The next one down is glm-4-9b at 46.9.

glm-4.7-flash, benchmark by benchmark

GPQA Diamond puts glm-4.7-flash at 75.2%, #130 of 275. That beats 53% of the models tested, but 20.6 points short of GPT-6 Astra (max) (95.8%). The results either side are NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16 (75.4%) and GPT-5 mini (high) (75.0%). It covers expert-level science questions. The board median is 71.7%, so glm-4.7-flash is 3.5 points above it. Source: HuggingFace GPQA Diamond leaderboard, captured 2026-10-13.

On OTIS Mock AIME, glm-4.7-flash reaches 58.3%. It ranks #94 of 189, ahead of 50% of the field, and 41.7 points behind Claude Fable 5.1 (max) at 100.0%. The results either side are QwQ-32B (59.2%) and Claude 3.7 Sonnet (64k thinking) (57.8%). It covers competition maths (AIME 2024-25 style). The board median is 57.8%, so glm-4.7-flash is 0.6 points above it. Source: Epoch AI Benchmarking Hub, captured 2026-10-13.

Chess Puzzles is a weak spot. glm-4.7-flash scores 0.0%, #105 of 122, behind 85% of the models measured. The leader, GPT-6 Astra (max), reaches 72.0%. It covers chess tactics puzzles. The board median is 12.5%, so glm-4.7-flash is 12.5 points below it. Source: Epoch AI Benchmarking Hub, captured 2026-10-13.

Strengths and weaknesses

Reasoning, maths and science: solid, ahead of 51% of the field on average across 2 benchmarks. Its best board here is GPQA Diamond (#130 of 275) and its weakest is OTIS Mock AIME (#94 of 189).

Price and value

The median input price across the 427 priced models is $0.500 per million tokens, so glm-4.7-flash costs less than the typical listing. Models that score within 1.5 points or better and cost less: Qwen2.5-Coder-7B-Instruct ($0.010, 49.5), DeepSeek V4.1 Flash (high) ($0.010, 48.9), DeepSeek V4 Flash 0731 (max) ($0.016, 53.1). Similar scores cost more on Devstral Small (2512) ($0.070) and DeepSeek v4 Flash (max) ($0.090).

Reading these numbers

The 3 scores on this page come from 2 sources: Epoch AI Benchmarking Hub (2), HuggingFace GPQA Diamond leaderboard (1). No official leaderboard supplied any of them, so treat them as third-party measurements. The leaderboard also lists 1 other reasoning-effort setting of this model, which redirect here. This page shows the highest. With 3 results the aggregate rank can move a lot when one new score arrives.

glm-4.7-flash is also listed at 1 other reasoning-effort setting; this page shows the highest-effort row, which is the one the leaderboard keeps by default.

Models ranked nearby

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#217 Claude Sonnet 4.6 (max) (50.0)
#218 grok-4-20 (50.0)