GLM 5.2 vs GLM 5.3 Flash
At a Glance
| Compare |  GLM 5.2Z.ai | GLM 5.3 FlashZ.ai |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v4.4 | UnrankedNot in the 57-model eligible cohort | #20 of 5756.2 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 37.5–70.8 |
| CostLower is better · Published-token output estimate | UnrankedNot in the 27-model eligible cohort | #2 of 27$0.0087 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.9 | UnrankedNot in the 27-model eligible cohort | #2 of 2765.8 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 56.4–73.1 |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.75Deepinfra ↗ · Oct 19, 2026 | $0.075Z.ai ↗ · Aug 29, 2026 |
| Output priceFrom · USD / 1M tokens | $2.40Deepinfra ↗ · Oct 19, 2026 | $0.25Z.ai ↗ · Aug 29, 2026 |
| Context windowMaximum documented tokens | 1,049K | 1,000K |
| Model facts checked | Aug 28, 2026View model evidence → | Sep 2, 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-Flash |
|---|---|---|
| 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 | 1,470.53100% of row best · rating · glm-5.3-flash; 95% CI [1465.67476412, 1475.38456118]; votes 32925; rank 29 |
| LiveBench2026-06-25-90f8d44da965 · overall · leader | 76.96100% of row best · percent · glm-5.2 · 23,463 output tokens / case | 73.2795% of row best · percent · glm-5.3-flash · 34,707 output tokens / case |
| ToneBench2026-10-17-10-task-e9c76358afb2 · overall_score · unverified | 75.7693% of row best · points · GLM-5.2 (high) · 11,138 output tokens / case | 81.82100% of row best · points · GLM-5.3 Flash (max) · 40,301 output tokens / case |
| Overall ResultCounted from the protocol-matched rows above · 1 tie | 1 benchmark winNo overall winner | 0 benchmark winsNo 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-Flash |
|---|---|---|
| Developer | Z.ai | Z.ai |
| Family | Glm 5 2 | Glm 5 3 Flash |
| Model | GLM-5.2 | GLM-5.3-Flash |
| Version | GLM-5.2 | GLM-5.3-Flash |
| Lifecycle | active | active |
| Released | Unknown | 2026-09-02 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text, Image, Video, Document |
| Output modalities | Text | Text |
| Context window | 1,049K | 1,000K |
| Total parameters | 753.3B | 320B |
| Active parameters | Unknown | 18B |
| License | mit | MIT |
| Open weights | Yes | Yes |
| API available | Yes | Yes |
| Self-hostable | Yes | Yes |
| 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, computer-use, reasoning, structured_outputs, tools, vision |
GLM 5.2 Capabilities
GLM 5.3 Flash Capabilities
Related Comparisons
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|---|---|---|---|
GLM-5.2Z.ai | vs | GLM-5.3Z.ai | family variantstext |
| 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 |
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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 |
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Primary Evidence
Sources and Freshness
Questions
GLM 5.2 vs GLM 5.3 Flash FAQs
Is GLM 5.2 or GLM 5.3 Flash better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both GLM 5.2 and GLM 5.3 Flash, 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 Flash?+
GLM 5.2 is $0.75 and GLM 5.3 Flash is $0.075 per million tokens, so GLM 5.3 Flash is cheaper on this metric. GLM 5.2 is $2.40 and GLM 5.3 Flash is $0.25 per million tokens, so GLM 5.3 Flash is cheaper on this metric.
Which has a larger context window, GLM 5.2 or GLM 5.3 Flash?+
GLM 5.2 has the larger sourced context window. GLM 5.2 supports 1,048,576 tokens and GLM 5.3 Flash supports 1,000,000 tokens.
Which performs better in benchmarks, GLM 5.2 or GLM 5.3 Flash?+
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 Flash be self-hosted?+
Both models have the same sourced self-hosting status: supported. GLM 5.2 is open weight; GLM 5.3 Flash is open weight.
Can GLM 5.2 and GLM 5.3 Flash understand images?+
GLM 5.2 is not documented with image input; GLM 5.3 Flash is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, GLM 5.2 or GLM 5.3 Flash?+
GLM 5.3 Flash 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 Flash support reasoning and tool use?+
GLM 5.2: reasoning and tool calling. GLM 5.3 Flash: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, GLM 5.2 or GLM 5.3 Flash?+
GLM 5.2 has 5 sourced provider routes; GLM 5.3 Flash has 4, so GLM 5.2 has broader tracked availability.
Which offers better value, GLM 5.2 or GLM 5.3 Flash?+
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.