GLM-5.2 vs GLM-5.3-Flash
Benchmark Performance
Available Benchmarks
| Benchmark | GLM-5.2 | GLM-5.3-Flash |
|---|---|---|
| LMArena Text Arenatext-2026-09-01-011508720696 · arena_rating · statistical tie | 1,466.80100% of row best · rating · glm-5.2-max; 95% CI [1462.10739564, 1471.49788772]; votes 33758; rank 26 | 1,470.91100% of row best · rating · glm-5.3-flash; 95% CI [1461.80526636, 1480.01283657]; votes 4399; rank 23 |
| LiveBench2026-06-25 · 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 |
| Overall ResultCounted from the protocol-matched rows above · 1 tie | 1 benchmark winOverall lead | 0 benchmark wins |
Third-party benchmark Only like-for-like primary-publisher results are shown; raw scores, relative scores, configuration, and token spend remain visible.
Technical Differences
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 | 1,048,576 | 1,000,000 |
| Total parameters | 753,329,940,480 | 320,000,000,000 |
| Active parameters | Unknown | 18,000,000,000 |
| License | mit | MIT |
| Open weights | Yes | Yes |
| API available | Yes | Yes |
| Self-hostable | Yes | Yes |
| Provider access | Deepinfra (Standard), Openrouter (Standard) | Deepinfra (Standard), Z.ai (Standard) |
| Capabilities | chat, generation, reasoning, tools | agents, chat, computer-use, reasoning, structured_outputs, tools, vision |
15 comparable fields · 9 material differences · Pair passes the primary-source comparison gate
GLM-5.2 Capabilities
GLM-5.3-Flash Capabilities
Internal Comparison Graph
Related Comparisons
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|---|---|---|---|
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GLM-5.3Z.ai | vs | GLM-5.3-FlashZ.ai | family variantstext |
GLM-5Z.ai | vs | GLM-5.2Z.ai | family variantstext |
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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 and GLM-5.3-Flash supports 1,000,000.
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 recorded 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?+
Neither has a larger sourced maximum output. GLM-5.2 is — and GLM-5.3-Flash is 131,072.
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 2 sourced provider routes; GLM-5.3-Flash has 2, a tie.
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.