Gemini 3.5 Flash-Lite vs GPT-5.2
Benchmark Performance
Available Benchmarks
| Benchmark | Gemini 3.5 Flash-Lite | GPT-5.2 |
|---|---|---|
| ARC-AGI-1verified-v1-b2e2a31b3d53 · verified_score · leader | 53.5062% of row best · percent · Gemini 3.5 Flash-Lite (High) | 86.17100% of row best · percent · GPT-5.2 (XHigh) |
| ARC-AGI-2verified-v2-326661568d5f · verified_score · leader | 10.2819% of row best · percent · Gemini 3.5 Flash-Lite (High) | 52.91100% of row best · percent · GPT-5.2 (XHigh) |
| LMArena Text Arenatext-2026-09-01-011508720696 · arena_rating · leader | 1,436.25100% of row best · rating · gemini-3.5-flash-lite; 95% CI [1431.05053629, 1441.44358777]; votes 21843; rank 74 | 1,412.4298% of row best · rating · gpt-5.2; 95% CI [1409.19172501, 1415.64592175]; votes 79073; rank 122 |
| LMArena Vision Arenavision-2026-08-27-011508720696 · arena_rating · leader | 1,268.89100% of row best · rating · gemini-3.5-flash-lite; 95% CI [1258.10808094, 1279.66830878]; votes 3886; rank 34 | 1,229.3697% of row best · rating · gpt-5.2; 95% CI [1223.13578833, 1235.59275214]; votes 19024; rank 63 |
| LiveBench2026-06-25 · overall · leader | 66.3584% of row best · percent · gemini-3.5-flash-lite-high · 11,526 output tokens / case | 79.21100% of row best · percent · gpt-5.2-2025-12-11-high · 11,512 output tokens / case |
| ToneBench2026-08-28-10-task-cd9819ab6e4d · overall_score · leader | 73.4294% of row best · points · Gemini 3.5 Flash-Lite · 1,966 output tokens / case | 78.08100% of row best · points · GPT-5.2 · 4,266 output tokens / case |
| Overall ResultCounted from the protocol-matched rows above | 2 benchmark wins | 4 benchmark winsOverall lead |
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 | Gemini 3.5 Flash-Lite | GPT-5.2 |
|---|---|---|
| Developer | Google DeepMind | OpenAI |
| Family | Gemini 3 | Gpt 5 2 |
| Model | Gemini 3.5 Flash-Lite | GPT-5.2 |
| Version | Gemini 3.5 Flash-Lite | GPT-5.2 |
| Lifecycle | active | active |
| Released | Unknown | 2025-12-11 |
| Knowledge cutoff | Unknown | 2025-08-31 |
| Input modalities | Text, Image, Video, Audio, Document | Text, Image |
| Output modalities | Text | Text |
| Context window | 1,048,576 | 400,000 |
| Total parameters | Unknown | Unknown |
| Active parameters | Unknown | Unknown |
| License | Unknown | Unknown |
| Open weights | No | No |
| API available | Yes | Yes |
| Self-hostable | No | No |
| Provider access | Google AI (Standard), Google Gemini (Standard) | Openai (Standard), Openrouter (Standard) |
| Capabilities | chat, generation, reasoning, tools | chat, generation, reasoning, structured_outputs, tools |
13 comparable fields · 8 material differences · Pair passes the primary-source comparison gate
Gemini 3.5 Flash-Lite Capabilities
GPT-5.2 Capabilities
Internal Comparison Graph
Related Comparisons
| A | Pair | B | Context |
|---|---|---|---|
GPT-5.1OpenAI | vs | GPT-5.2OpenAI | family variantsimage, text |
GPT-5 ProOpenAI | vs | GPT-5.2OpenAI | family variantsimage, text |
GPT-5.2OpenAI | vs | GPT-5.4 MiniOpenAI | family variantsimage, text |
GPT-5.2OpenAI | vs | GPT-5.3-CodexOpenAI | family variantsimage, text |
GPT-5.2OpenAI | vs |  Qwen3 VL FlashQwen | cross-developer peersimage, text |
Gemma 4 E4BGoogle Deepmind | vs | GPT-5.2OpenAI | cross-developer peersimage, text |
GPT-4oOpenAI | vs | GPT-5.2OpenAI | family variantsimage, text |
GPT-4o MiniOpenAI | vs | GPT-5.2OpenAI | family variantsimage, text |
Seed 2.0 ProByteDance Seed | vs | GPT-5.2OpenAI | cross-developer peersimage, text |
Seed 2.1 TurboByteDance Seed | vs | GPT-5.2OpenAI | cross-developer peersimage, text |
Seed 2.0 CodeByteDance Seed | vs | GPT-5.2OpenAI | cross-developer peerstext |
| vs | GPT-5.2OpenAI | cross-developer peerstext |
Primary Evidence
Sources and Freshness
Questions
Gemini 3.5 Flash-Lite vs GPT-5.2 FAQs
Is Gemini 3.5 Flash-Lite or GPT-5.2 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Gemini 3.5 Flash-Lite and GPT-5.2, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Gemini 3.5 Flash-Lite or GPT-5.2?+
Gemini 3.5 Flash-Lite is $0.30 and GPT-5.2 is $1.75 per million tokens, so Gemini 3.5 Flash-Lite is cheaper on this metric. Gemini 3.5 Flash-Lite is $2.50 and GPT-5.2 is $14.00 per million tokens, so Gemini 3.5 Flash-Lite is cheaper on this metric.
Which has a larger context window, Gemini 3.5 Flash-Lite or GPT-5.2?+
Gemini 3.5 Flash-Lite has the larger sourced context window. Gemini 3.5 Flash-Lite supports 1,048,576 and GPT-5.2 supports 400,000.
Which performs better in benchmarks, Gemini 3.5 Flash-Lite or GPT-5.2?+
GPT-5.2 leads the current overall benchmark count. The result uses 6 protocol-matched benchmarks from 4 publishers; it is not a universal quality score.
Can Gemini 3.5 Flash-Lite or GPT-5.2 be self-hosted?+
Both models have the same recorded self-hosting status: unsupported. Gemini 3.5 Flash-Lite is not marked open weight; GPT-5.2 is not marked open weight.
Can Gemini 3.5 Flash-Lite and GPT-5.2 understand images?+
Gemini 3.5 Flash-Lite is documented with image input; GPT-5.2 is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Gemini 3.5 Flash-Lite or GPT-5.2?+
GPT-5.2 has the larger sourced maximum output: Gemini 3.5 Flash-Lite supports 65,536 and GPT-5.2 supports 128,000 output tokens.
Do Gemini 3.5 Flash-Lite and GPT-5.2 support reasoning and tool use?+
Gemini 3.5 Flash-Lite: reasoning, tool calling, and image input. GPT-5.2: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Gemini 3.5 Flash-Lite or GPT-5.2?+
Gemini 3.5 Flash-Lite has 2 sourced provider routes; GPT-5.2 has 2, a tie.
Which offers better value, Gemini 3.5 Flash-Lite or GPT-5.2?+
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.