GPT-4o vs GPT-5.2
Benchmark Performance
Available Benchmarks
| Benchmark | GPT-4o | GPT-5.2 |
|---|---|---|
| LMArena Text Arenatext-2026-09-01-011508720696 · arena_rating · leader | 1,282.3391% of row best · rating · gpt-4o-2024-08-06; 95% CI [1278.14385443, 1286.52509361]; votes 45499; rank 250 | 1,412.42100% 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 | 1,064.5487% of row best · rating · gpt-4o-2024-08-06; 95% CI [1052.10281463, 1076.97369812]; votes 3376; rank 119 | 1,229.36100% of row best · rating · gpt-5.2; 95% CI [1223.13578833, 1235.59275214]; votes 19024; rank 63 |
| ToneBench2026-08-28-10-task-cd9819ab6e4d · overall_score · leader | 58.6675% of row best · points · GPT-4o · 1,174 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 | 0 benchmark wins | 3 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 | GPT-4o | GPT-5.2 |
|---|---|---|
| Developer | OpenAI | OpenAI |
| Family | Gpt 4o | Gpt 5 2 |
| Model | GPT-4o | GPT-5.2 |
| Version | GPT-4o | GPT-5.2 |
| Lifecycle | active | active |
| Released | Unknown | 2025-12-11 |
| Knowledge cutoff | 2023-10-01 | 2025-08-31 |
| Input modalities | Text, Image | Text, Image |
| Output modalities | Text | Text |
| Context window | 128,000 | 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 | Openai (Standard), Openrouter (Standard) | Openai (Standard), Openrouter (Standard) |
| Capabilities | chat, generation, tools | chat, generation, reasoning, structured_outputs, tools |
14 comparable fields · 6 material differences · Pair passes the primary-source comparison gate
GPT-4o Capabilities
GPT-5.2 Capabilities
Internal Comparison Graph
Related Comparisons
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Primary Evidence
Sources and Freshness
Questions
GPT-4o vs GPT-5.2 FAQs
Is GPT-4o or GPT-5.2 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both GPT-4o and GPT-5.2, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, GPT-4o or GPT-5.2?+
GPT-4o is $1.25 and GPT-5.2 is $1.75 per million tokens, so GPT-4o is cheaper on this metric. GPT-4o is $5.00 and GPT-5.2 is $14.00 per million tokens, so GPT-4o is cheaper on this metric.
Which has a larger context window, GPT-4o or GPT-5.2?+
GPT-5.2 has the larger sourced context window. GPT-4o supports 128,000 and GPT-5.2 supports 400,000.
Which performs better in benchmarks, GPT-4o or GPT-5.2?+
GPT-5.2 leads the current overall benchmark count. The result uses 3 protocol-matched benchmarks from 2 publishers; it is not a universal quality score.
Can GPT-4o or GPT-5.2 be self-hosted?+
Both models have the same recorded self-hosting status: unsupported. GPT-4o is not marked open weight; GPT-5.2 is not marked open weight.
Can GPT-4o and GPT-5.2 understand images?+
GPT-4o 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, GPT-4o or GPT-5.2?+
GPT-5.2 has the larger sourced maximum output: GPT-4o supports 16,384 and GPT-5.2 supports 128,000 output tokens.
Do GPT-4o and GPT-5.2 support reasoning and tool use?+
GPT-4o: 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, GPT-4o or GPT-5.2?+
GPT-4o has 2 sourced provider routes; GPT-5.2 has 2, a tie.
Which offers better value, GPT-4o 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.