Gemini 3.1 Flash Lite vs GLM 5.3 Flash
At a Glance
| Compare |  Gemini 3.1 Flash LiteGoogle DeepMind |  GLM 5.3 FlashZ.ai |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | UnrankedNot in the 46-model eligible cohort | #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 | UnrankedNot in the 44-model eligible cohort | #2 of 44$0.0087 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.5 | UnrankedNot in the 38-model eligible cohort | #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 | $0.25Google AI ↗ · Aug 29, 2026 | $0.075Z.ai ↗ · Aug 29, 2026 |
| Output priceFrom · USD / 1M tokens | $1.50Google 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 3.1 Flash-Lite | GLM-5.3-Flash |
|---|---|---|
| ToneBench2026-09-11-10-task-4ef099199c9c · overall_score · unverified | 71.3081% of row best · points · Gemini 3.1 Flash-Lite · 1,628 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 | 0 benchmark winsNo 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 | Gemini 3.1 Flash-Lite | GLM-5.3-Flash |
|---|---|---|
| Developer | Google DeepMind | Z.ai |
| Family | Gemini 3 | Glm 5 3 Flash |
| Model | Gemini 3.1 Flash-Lite | GLM-5.3-Flash |
| Version | Gemini 3.1 Flash-Lite | 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 3.1 Flash Lite Capabilities
GLM 5.3 Flash Capabilities
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|---|---|---|---|
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Gemini 3.1 Flash-LiteGoogle DeepMind | vs | Grok 4.6xAI | cross-developer peerstext |
Gemini 3.1 Flash-LiteGoogle DeepMind | vs |  Qwen3.8-MaxQwen | cross-developer peerstext |
Gemini 3.1 Flash-LiteGoogle DeepMind | vs |  Kimi-K3Moonshot AI | cross-developer peerstext |
 MiniMax-M3MiniMax | vs | Gemini 3.1 Flash-LiteGoogle DeepMind | cross-developer peerstext |
Gemini 3.1 Flash-LiteGoogle DeepMind | vs | GLM-5.3Z.ai | cross-developer peerstext |
Gemini 3.1 Flash-LiteGoogle DeepMind | vs |  Hy4 previewTencent | cross-developer peerstext |
Seed 2.1 ProByteDance Seed | vs | Gemini 3.1 Flash-LiteGoogle DeepMind | cross-developer peerstext |
Gemini 3.1 Flash-LiteGoogle DeepMind | vs |  Mistral Large 3Mistral AI | cross-developer peerstext |
Primary Evidence
Sources and Freshness
Questions
Gemini 3.1 Flash Lite vs GLM 5.3 Flash FAQs
Is Gemini 3.1 Flash Lite or GLM 5.3 Flash better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Gemini 3.1 Flash Lite 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 3.1 Flash Lite or GLM 5.3 Flash?+
Gemini 3.1 Flash Lite is $0.25 and GLM 5.3 Flash is $0.075 per million tokens, so GLM 5.3 Flash is cheaper on this metric. Gemini 3.1 Flash Lite is $1.50 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 3.1 Flash Lite or GLM 5.3 Flash?+
Gemini 3.1 Flash Lite has the larger sourced context window. Gemini 3.1 Flash Lite supports 1,049K and GLM 5.3 Flash supports 1,000K.
Which performs better in benchmarks, Gemini 3.1 Flash Lite or GLM 5.3 Flash?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Gemini 3.1 Flash Lite 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 3.1 Flash Lite is not marked open weight; GLM 5.3 Flash is open weight.
Can Gemini 3.1 Flash Lite and GLM 5.3 Flash understand images?+
Gemini 3.1 Flash Lite 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 3.1 Flash Lite or GLM 5.3 Flash?+
GLM 5.3 Flash has the larger sourced maximum output: Gemini 3.1 Flash Lite supports 66K and GLM 5.3 Flash supports 131K output tokens.
Do Gemini 3.1 Flash Lite and GLM 5.3 Flash support reasoning and tool use?+
Gemini 3.1 Flash Lite: 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 3.1 Flash Lite or GLM 5.3 Flash?+
Gemini 3.1 Flash Lite 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 3.1 Flash Lite 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.