Head-to-head comparison
Gemini 3.7 Flash vs. GLM-5.2
Compare published benchmark results from matching versions and cohorts, API pricing, and technical specifications. Any documented test setup differences remain visible.
7
shared result series
7
distinct benchmarks
2026-08-16
latest retrieval
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Specifications and cost
General specifications and standard API pricing
Undisclosed parameter counts and knowledge cutoffs stay marked as unavailable. The table shows direct standard token pricing, including documented context tiers. Batch, fast, priority, regional surcharges, tool calls, and cloud platform pricing are excluded.
| Attribute | Gemini 3.7 Flash | GLM-5.2 |
|---|---|---|
| Provider | Z.ai | |
| Released | Aug 13, 2026 | Jun 16, 2026 |
| Availability | Active | Open Source |
| Model type | Proprietary | Open Weights |
| Parameters | Not published | 744B, 40B active |
| Architecture | Not published | Mixture of Experts |
| Context window | 1,048,576 tokens | 1,000,000 tokens |
| Knowledge cutoff | March 2026 | Not published |
| Technical sources | Model, Knowledge cutoff | Model, Context |
| API pricing per 1M tokens | ||
| API input | $0.75 | $1.4 |
| API output | $3.75 | $4.4 |
| Cache read | $0.07 | $0.26 |
| Cache write (5 min.) | Not listed | Not listed |
| Cache write (1 hr.) | Not listed | Not listed |
| Price verified | 08/16/2026Source | 07/30/2026Source |
Performance
Shared benchmarks
Only published results with the same benchmark version, task, metric, and comparison cohort are paired. Different reasoning levels, agents, harnesses, or output limits appear directly in the table.
| Benchmark and task | Gemini 3.7 Flash | GLM-5.2 | Comparability | Source |
|---|---|---|---|---|
| EMBOverall | 71.326 %Winner | 61.532 % | 1accuracyHigher is betterCohort: vals-emb:1:overallNo documented setup differenceWinner: Gemini 3.7 FlashGemini 3.7 Flash: No further settings disclosed GLM-5.2: No further settings disclosed | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-13Import ID: vals-emb-2026-08-16-68d5d1d47a97Retrieved 2026-08-16 |
| Finance Agent (v2)Overall | 59.042 %Winner | 49.699 % | 2accuracyHigher is betterCohort: vals-finance-agent-v2:2:overallNo documented setup differenceWinner: Gemini 3.7 FlashGemini 3.7 Flash: No further settings disclosed GLM-5.2: No further settings disclosed | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-14Import ID: vals-fabv2-2026-08-16-8853da43085dRetrieved 2026-08-16 |
| Harvey's Legal Agent BenchmarkOverall · Task fully resolved | 8.75 %Winner | 7.083 % | 1task resolution rateHigher is betterCohort: vals-legal-agent-benchmark:1:overallNo documented setup differenceWinner: Gemini 3.7 FlashGemini 3.7 Flash: No further settings disclosed GLM-5.2: No further settings disclosed | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-14Import ID: vals-hlab-2026-08-16-bae90566df2fRetrieved 2026-08-16 |
| Legal Research BenchOverall · All-pass | 34.615 %Winner | 31.25 % | 1all-pass rateHigher is betterCohort: vals-legal-research:1:overallNo documented setup differenceWinner: Gemini 3.7 FlashGemini 3.7 Flash: No further settings disclosed GLM-5.2: No further settings disclosed | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-14Import ID: vals-legal_research-2026-08-16-af296c00ecaaRetrieved 2026-08-16 |
| Terminal-Bench 2.1Overall | 77.528 %Winner | 67.79 % | 2.1accuracyHigher is betterCohort: vals-terminal-bench-2-1:2.1:overallNo documented setup differenceWinner: Gemini 3.7 FlashGemini 3.7 Flash: No further settings disclosed GLM-5.2: No further settings disclosed | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-13Import ID: vals-terminal-bench-2-1-2026-08-16-b3f38c18bea4Retrieved 2026-08-16 |
| Vals IndexOverall | 59.307 %Winner | 53.122 % | 2weighted index scoreHigher is betterCohort: vals-vals-index:2:overallNo documented setup differenceWinner: Gemini 3.7 FlashGemini 3.7 Flash: No further settings disclosed GLM-5.2: No further settings disclosed | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-14Import ID: vals-vals_index-2026-08-16-ab641d89d6edRetrieved 2026-08-16 |
| Vibe Code Bench v1.1Overall | 70.395 %Winner | 63.961 % | 1.1accuracyHigher is betterCohort: vals-vibe-code-bench:1.1:overallNo documented setup differenceWinner: Gemini 3.7 FlashGemini 3.7 Flash: Harness: OpenHands GLM-5.2: Harness: OpenHands | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-13Import ID: vals-vibe-code-2026-08-16-a73c9770bcc4Retrieved 2026-08-16 |
How to read this comparison
Winning one benchmark is not an overall verdict
Your workload, budget, and required context length matter most. A coding benchmark says little about visual understanding or agent performance.
Prices use official API rates per 1M tokens. Web subscriptions and cloud platform surcharges can differ.
Every result links to its measurement source and retrieval date. Labels distinguish vendor reports, official benchmark leaderboards, and independent evaluations. Disputed or archived results are excluded.
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