Gemini 3.7 Flash vs GLM 5.3 Flash
At a Glance
| Compare |  Gemini 3.7 FlashGoogle DeepMind |  GLM 5.3 FlashZ.ai |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | #4 of 4687.7 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 | #17 of 44$0.085 per LiveBench case | #2 of 44$0.0087 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.5 | #2 of 3869.9 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 | $0.75Google AI ↗ · Aug 29, 2026 | $0.075Z.ai ↗ · Aug 29, 2026 |
| Output priceFrom · USD / 1M tokens | $3.75Google AI ↗ · Aug 29, 2026 | $0.25Z.ai ↗ · Aug 29, 2026 |
| Context windowMaximum documented tokens | 1,049K | 1,000K |
| Model facts checked | Aug 29, 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 | Gemini 3.7 Flash | GLM-5.3-Flash |
|---|---|---|
| LMArena Agent Arenaagent-2026-09-15-d25aabda0010 · outcome_score · leader | -0.6098% of row best · score · Gemini 3.7 Flash (High); 95% CI [-1.25249139, 0.04302776]; sessions 48195; observations 3925675; rank 28 | 1.15100% 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 · leader | 1,490.49100% of row best · rating · gemini-3.7-flash-high; 95% CI [1482.31296820, 1498.67106273]; votes 5640; rank 8 | 1,471.8999% of row best · rating · glm-5.3-flash; 95% CI [1465.37026588, 1478.41920488]; votes 10038; rank 24 |
| LiveBench2026-06-25 · overall · leader | 83.15100% of row best · percent · gemini-3.7-flash-high · 22,610 output tokens / case | 73.2788% of row best · percent · glm-5.3-flash · 34,707 output tokens / case |
| ToneBench2026-09-11-10-task-4ef099199c9c · overall_score · unverified | 77.9588% of row best · points · Gemini 3.7 Flash (high thinking) · 9,139 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 benchmark winsOverall lead | 1 benchmark win |
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 | Gemini 3.7 Flash | GLM-5.3-Flash |
|---|---|---|
| Developer | Google DeepMind | Z.ai |
| Family | Gemini 3 | Glm 5 3 Flash |
| Model | Gemini 3.7 Flash | GLM-5.3-Flash |
| Version | Gemini 3.7 Flash | GLM-5.3-Flash |
| Lifecycle | active | active |
| Released | Unknown | 2026-09-02 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image, Video, Audio, Document | Text, Image, Video, Document |
| Output modalities | Text | Text |
| Context window | 1,049K | 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 | Google AI (Standard), Google Gemini (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 |
Gemini 3.7 Flash Capabilities
GLM 5.3 Flash Capabilities
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Primary Evidence
Sources and Freshness
Questions
Gemini 3.7 Flash vs GLM 5.3 Flash FAQs
Is Gemini 3.7 Flash or GLM 5.3 Flash better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Gemini 3.7 Flash and GLM 5.3 Flash, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Gemini 3.7 Flash or GLM 5.3 Flash?+
Gemini 3.7 Flash is $0.75 and GLM 5.3 Flash is $0.075 per million tokens, so GLM 5.3 Flash is cheaper on this metric. Gemini 3.7 Flash is $3.75 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, Gemini 3.7 Flash or GLM 5.3 Flash?+
Gemini 3.7 Flash has the larger sourced context window. Gemini 3.7 Flash supports 1,049K and GLM 5.3 Flash supports 1,000K.
Which performs better in benchmarks, Gemini 3.7 Flash or GLM 5.3 Flash?+
Gemini 3.7 Flash leads the current overall benchmark count. The result uses 3 protocol-matched benchmarks from 2 publishers; it is not a universal quality score.
Can Gemini 3.7 Flash or GLM 5.3 Flash be self-hosted?+
GLM 5.3 Flash is the only model in this pair currently marked as self-hostable. Gemini 3.7 Flash is not marked open weight; GLM 5.3 Flash is open weight.
Can Gemini 3.7 Flash and GLM 5.3 Flash understand images?+
Gemini 3.7 Flash 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, Gemini 3.7 Flash or GLM 5.3 Flash?+
GLM 5.3 Flash has the larger sourced maximum output: Gemini 3.7 Flash supports 66K and GLM 5.3 Flash supports 131K output tokens.
Do Gemini 3.7 Flash and GLM 5.3 Flash support reasoning and tool use?+
Gemini 3.7 Flash: 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, Gemini 3.7 Flash or GLM 5.3 Flash?+
Gemini 3.7 Flash has 2 sourced provider routes; GLM 5.3 Flash has 4, so GLM 5.3 Flash has broader tracked availability.
Which offers better value, Gemini 3.7 Flash 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.