GLM 5.2 vs GLM 5.3
At a Glance
| Compare |  GLM 5.2Z.ai | GLM 5.3Z.ai |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v4.4 | UnrankedNot in the 57-model eligible cohort | #14 of 5771.9 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 47.9–81.3 |
| CostLower is better · Published-token output estimate | UnrankedNot in the 27-model eligible cohort | #22 of 27$0.248 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.9 | UnrankedNot in the 27-model eligible cohort | #19 of 2745.9 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 33.9–50.6 |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.75Deepinfra ↗ · Oct 19, 2026 | $0.90Deepinfra ↗ · Oct 19, 2026 |
| Output priceFrom · USD / 1M tokens | $2.40Deepinfra ↗ · Oct 19, 2026 | $4.00Deepinfra ↗ · Oct 19, 2026 |
| Context windowMaximum documented tokens | 1,049K | 1,000K |
| Model facts checked | Aug 28, 2026View model evidence → | Aug 29, 2026View model evidence → |
Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →
Available Benchmarks
| Benchmark | GLM-5.2 | GLM-5.3 |
|---|---|---|
| LMArena Text Arenatext-2026-10-15-a64ca3b12b6e · arena_rating · statistical tie | 1,470.37100% of row best · rating · glm-5.2-max; 95% CI [1466.22224854, 1474.51957977]; votes 49820; rank 31 | 1,471.46100% of row best · rating · glm-5.3-max; 95% CI [1466.26042381, 1476.65163576]; votes 22543; rank 27 |
| LiveBench2026-06-25-90f8d44da965 · overall · leader | 76.9697% of row best · percent · glm-5.2 · 23,463 output tokens / case | 79.15100% of row best · percent · glm-5.3 · 62,090 output tokens / case |
| ToneBench2026-10-17-10-task-e9c76358afb2 · overall_score · unverified | 75.7692% of row best · points · GLM-5.2 (high) · 11,138 output tokens / case | 82.39100% of row best · points · GLM-5.3 (max) · 50,208 output tokens / case |
| Overall ResultCounted from the protocol-matched rows above · 1 tie | 0 benchmark winsNo overall winner | 1 benchmark winNo overall winner |
Third-party benchmark Only like-for-like primary-publisher results are shown; raw scores, relative scores, configuration, and token spend remain visible.
Side-by-Side Facts
| Field | GLM-5.2 | GLM-5.3 |
|---|---|---|
| Developer | Z.ai | Z.ai |
| Family | Glm 5 2 | Glm 5 3 |
| Model | GLM-5.2 | GLM-5.3 |
| Version | GLM-5.2 | GLM-5.3 |
| Lifecycle | active | active |
| Released | Unknown | Unknown |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text |
| Output modalities | Text | Text |
| Context window | 1,049K | 1,000K |
| Total parameters | 753.3B | Unknown |
| Active parameters | Unknown | Unknown |
| License | mit | Unknown |
| Open weights | Yes | No |
| API available | Yes | Yes |
| Self-hostable | Yes | No |
| Provider access | Deepinfra (Standard), Fireworks Ai (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard) | Deepinfra (Standard), Fireworks Ai (Standard), Together Ai (Standard), Z.ai (Standard) |
| Capabilities | chat, generation, reasoning, tools | agents, chat, reasoning, structured_outputs, tools |
GLM 5.2 Capabilities
GLM 5.3 Capabilities
Related Comparisons
All text comparisons →| A | Pair | B | Context |
|---|---|---|---|
| vs | GLM-5.2Z.ai | cross-developer peerstext | |
 DeepSeek-V4-Flash-BaseDeepSeek | vs | GLM-5.2Z.ai | cross-developer peerstext |
DeepSeek-V4-ProDeepSeek | vs | GLM-5.2Z.ai | cross-developer peerstext |
DeepSeek-V4-Pro-BaseDeepSeek | vs | GLM-5.2Z.ai | cross-developer peerstext |
GLM-5.2Z.ai | vs | GLM-5.3-FlashZ.ai | family variantstext |
DeepSeek-V3.2DeepSeek | vs | GLM-5.2Z.ai | cross-developer peerstext |
DeepSeek-V4-FlashDeepSeek | vs | GLM-5.2Z.ai | cross-developer peerstext |
DeepSeek-V4-Pro-0813DeepSeek | vs | GLM-5.2Z.ai | cross-developer peerstext |
 Kimi-K2-ThinkingMoonshot AI | vs | GLM-5.2Z.ai | cross-developer peerstext |
GLM-5Z.ai | vs | GLM-5.2Z.ai | family variantstext |
GLM-5.1Z.ai | vs | GLM-5.2Z.ai | family variantstext |
Primary Evidence
Sources and Freshness
Questions
GLM 5.2 vs GLM 5.3 FAQs
Is GLM 5.2 or GLM 5.3 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both GLM 5.2 and GLM 5.3, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, GLM 5.2 or GLM 5.3?+
GLM 5.2 is $0.75 and GLM 5.3 is $0.90 per million tokens, so GLM 5.2 is cheaper on this metric. GLM 5.2 is $2.40 and GLM 5.3 is $4.00 per million tokens, so GLM 5.2 is cheaper on this metric.
Which has a larger context window, GLM 5.2 or GLM 5.3?+
GLM 5.2 has the larger sourced context window. GLM 5.2 supports 1,048,576 tokens and GLM 5.3 supports 1,000,000 tokens.
Which performs better in benchmarks, GLM 5.2 or GLM 5.3?+
There is no overall benchmark winner: An overall winner requires at least two decisive benchmarks from at least two original publishers.
Can GLM 5.2 or GLM 5.3 be self-hosted?+
GLM 5.2 is documented as self-hostable; GLM 5.3 is documented as not self-hostable. GLM 5.2 is open weight; GLM 5.3 is not marked open weight.
Can GLM 5.2 and GLM 5.3 understand images?+
GLM 5.2 is not documented with image input; GLM 5.3 is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, GLM 5.2 or GLM 5.3?+
GLM 5.3 has a sourced maximum output of 131,072 tokens; GLM 5.2's maximum output is not reported. A larger limit cannot be determined.
Do GLM 5.2 and GLM 5.3 support reasoning and tool use?+
GLM 5.2: reasoning and tool calling. GLM 5.3: reasoning and tool calling. Feature support does not establish relative quality.
Which is available from more inference providers, GLM 5.2 or GLM 5.3?+
GLM 5.2 has 5 sourced provider routes; GLM 5.3 has 4, so GLM 5.2 has broader tracked availability.
Which offers better value, GLM 5.2 or GLM 5.3?+
There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.