Z.ai, released Apr 7, 2026
GLM-5.1price, context, benchmarks and release details
- Input, per 1M tokens
- $1.40Z.ai API pricingOfficial Z.ai global Standard uncached token rate; coding-plan subscriptions, cache and Batch excluded
Retrieved Oct 9, 2026 · factual citation
Open source ↗ - Output, per 1M tokens
- $4.40Z.ai API pricingOfficial Z.ai global Standard uncached token rate; coding-plan subscriptions, cache and Batch excluded
Retrieved Oct 9, 2026 · factual citation
Open source ↗ - Context window
- 200Kmodels.devPublished source fact
Retrieved Oct 9, 2026 · MIT
Open source ↗ - Max output
- 131Kmodels.devPublished source fact
Retrieved Oct 9, 2026 · MIT
Open source ↗ - Released
- Apr 7, 2026models.devPublished source fact
Retrieved Oct 9, 2026 · MIT
Open source ↗
How the score breaks down
Weights: reasoning 30%, math 15%, coding 40%, preference 15%. Results use fixed 0–100 scales before averaging, and thin evidence is pulled toward 50. Method si-v3-retained-evidence-2, computed Oct 9, 2026, 05:13 UTC.
Around it on the leaderboard
- 41 GPT-5.6 Luna 63.4
- 42 Gemma 4 26B A4B IT 63.4
- 43 GLM-5.1 63.3
- 44 DeepSeek V4 Pro 63.1
- 45 Gemma 4 31B IT 63.0
Benchmark results
5 benchmarks, 6 resultsEach row shows the best published result. Where a model was tested at several settings, such as reasoning effort, open the row to see each one. Hover or tap a value for its source.
Open source ↗ 89.9
FrontierMath Tiers 1–3 (v2)math 36.8%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] Published Aug 29, 2026
Retrieved Oct 9, 2026 · CC-BY
Open source ↗
36.8 2 settings
- no reasoning 24.9%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] nonePublished Aug 28, 2026
Retrieved Oct 9, 2026 · CC-BY
Open source ↗ - Setting 2 36.8%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] Published Aug 29, 2026
Retrieved Oct 9, 2026 · CC-BY
Open source ↗
Open source ↗ 93.3
Open source ↗ 74.2
Open source ↗ 78.7
Normalization uses a fixed 0–100 scale for each unit, independent of other models. Compare evaluation conditions before reading a small gap as decisive. “Lab-reported” marks the provider's own published figure.
Details and sources
- Open weights
- YesHugging Face HubPublic Hub repo with weight files; gating/repo upload date is not release date
Retrieved Oct 9, 2026 · factual metadata; model-specific licenses
Open source ↗ - License
- mitHugging Face HubPublished source fact
Retrieved Oct 9, 2026 · factual metadata; model-specific licenses
Open source ↗ - Input modalities
- textmodels.devPublished source fact
Retrieved Oct 9, 2026 · MIT
Open source ↗ - First seen by SuperIndex
- Oct 8, 2026
- Coverage
- 51% of expected source weight
Reported (2)
- Epoch AI BenchmarkingOct 8, 2026
- LMArena / ArenaOct 8, 2026
Awaiting (5)
- ARC Prize13% of weight
- Humanity’s Last Exam13% of weight
- Official model cards via models.dev4% of weight
- LiveBench13% of weight
- Terminal-Bench6% of weight
Confidence rises as pending sources publish. Some sources never cover some models, so confidence reaches 100% at 80% of expected weight.