GPT-5.5 vs GLM 5.3 Flash
At a Glance
| Compare |  GPT-5.5OpenAI |  GLM 5.3 FlashZ.ai |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | #7 of 4683.9 score · 3/3 sources · complete | #29 of 4654.6 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 36.4–69.8 |
| CostLower is better · Published-token output estimate | #38 of 44$0.341 per LiveBench case | #2 of 44$0.0087 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.5 | #18 of 3853.4 score · 3/3 sources · complete | #1 of 3877.3 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 68.2–84.9 |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $5.00Openai ↗ · Sep 3, 2026 | $0.075Z.ai ↗ · Aug 29, 2026 |
| Output priceFrom · USD / 1M tokens | $30.00Openai ↗ · Sep 3, 2026 | $0.25Z.ai ↗ · Aug 29, 2026 |
| Context windowMaximum documented tokens | 1,050K | 1,000K |
| Model facts checked | Sep 3, 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 | GPT-5.5 | GLM-5.3-Flash |
|---|---|---|
| LMArena Agent Arenaagent-2026-09-15-d25aabda0010 · outcome_score · leader | 2.67100% of row best · score · GPT 5.5; 95% CI [1.85777151, 3.48040109]; sessions 81254; observations 2107641; rank 20 | 1.1599% of row best · score · GLM 5.3 Flash; 95% CI [0.48275443, 1.81278415]; sessions 43164; observations 4433017; rank 25 |
| LMArena Text Arenatext-2026-09-13-d25aabda0010 · arena_rating · statistical tie | 1,465.59100% of row best · rating · gpt-5.5; 95% CI [1461.82849712, 1469.35015015]; votes 66317; rank 31 | 1,471.89100% of row best · rating · glm-5.3-flash; 95% CI [1465.37026588, 1478.41920488]; votes 10038; rank 24 |
| LMArena Vision Arenavision-2026-09-13-d25aabda0010 · arena_rating · statistical tie | 1,294.19100% of row best · rating · gpt-5.5-high; 95% CI [1287.85384790, 1300.52893287]; votes 21960; rank 20 | 1,298.71100% of row best · rating · glm-5.3-flash; 95% CI [1287.06707337, 1310.34654848]; votes 3110; rank 17 |
| LiveBench2026-06-25 · overall · leader | 84.54100% of row best · percent · gpt-5.5-xhigh · 11,355 output tokens / case | 73.2787% of row best · percent · glm-5.3-flash · 34,707 output tokens / case |
| ToneBench2026-09-11-10-task-4ef099199c9c · overall_score · unverified | 82.6094% of row best · points · GPT-5.5 (xhigh) · 4,050 output tokens / case | 88.19100% of row best · points · GLM-5.3 Flash · 25,960 output tokens / case |
| Overall ResultCounted from the protocol-matched rows above · 2 ties | 2 benchmark winsOverall 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.
Side-by-Side Facts
| Field | GPT-5.5 | GLM-5.3-Flash |
|---|---|---|
| Developer | OpenAI | Z.ai |
| Family | Gpt 5 5 | Glm 5 3 Flash |
| Model | GPT-5.5 | GLM-5.3-Flash |
| Version | GPT-5.5 | GLM-5.3-Flash |
| Lifecycle | active | active |
| Released | 2026-04-23 | 2026-09-02 |
| Knowledge cutoff | 2025-12-01 | Unknown |
| Input modalities | Text, Image | Text, Image, Video, Document |
| Output modalities | Text | Text |
| Context window | 1,050K | 1,000K |
| Total parameters | Unknown | 320B |
| Active parameters | Unknown | 18B |
| License | Unknown | MIT |
| Open weights | No | Yes |
| API available | Yes | Yes |
| Self-hostable | No | Yes |
| Provider access | Openai (Standard), Openrouter (Standard) | Deepinfra (Standard), Fireworks Ai (Standard), Together Ai (Standard), Z.ai (Standard) |
| Capabilities | chat, generation, reasoning, structured_outputs, tools | agents, chat, computer-use, reasoning, structured_outputs, tools, vision |
GPT-5.5 Capabilities
GLM 5.3 Flash Capabilities
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Primary Evidence
Sources and Freshness
Questions
GPT-5.5 vs GLM 5.3 Flash FAQs
Is GPT-5.5 or GLM 5.3 Flash better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both GPT-5.5 and GLM 5.3 Flash, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, GPT-5.5 or GLM 5.3 Flash?+
GPT-5.5 is $5.00 and GLM 5.3 Flash is $0.075 per million tokens, so GLM 5.3 Flash is cheaper on this metric. GPT-5.5 is $30.00 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, GPT-5.5 or GLM 5.3 Flash?+
GPT-5.5 has the larger sourced context window. GPT-5.5 supports 1,050K and GLM 5.3 Flash supports 1,000K.
Which performs better in benchmarks, GPT-5.5 or GLM 5.3 Flash?+
GPT-5.5 leads the current overall benchmark count. The result uses 4 protocol-matched benchmarks from 2 publishers; it is not a universal quality score.
Can GPT-5.5 or GLM 5.3 Flash be self-hosted?+
GLM 5.3 Flash is the only model in this pair currently marked as self-hostable. GPT-5.5 is not marked open weight; GLM 5.3 Flash is open weight.
Can GPT-5.5 and GLM 5.3 Flash understand images?+
GPT-5.5 is 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, GPT-5.5 or GLM 5.3 Flash?+
GLM 5.3 Flash has the larger sourced maximum output: GPT-5.5 supports 128K and GLM 5.3 Flash supports 131K output tokens.
Do GPT-5.5 and GLM 5.3 Flash support reasoning and tool use?+
GPT-5.5: reasoning, tool calling, and image input. GLM 5.3 Flash: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, GPT-5.5 or GLM 5.3 Flash?+
GPT-5.5 has 2 sourced provider routes; GLM 5.3 Flash has 4, so GLM 5.3 Flash has broader tracked availability.
Which offers better value, GPT-5.5 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.