DeepSeek V3.2 vs GPT-5.3 Codex
At a Glance
| Compare |  DeepSeek V3.2DeepSeek |  GPT-5.3 CodexOpenAI |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.26Deepinfra ↗ · Sep 23, 2026 | $1.75Openai ↗ · Sep 3, 2026 |
| Output priceFrom · USD / 1M tokens | $0.38Deepinfra ↗ · Sep 23, 2026 | $14.00Openai ↗ · Sep 3, 2026 |
| Context windowMaximum documented tokens | 164K | 400K |
| Model facts checked | Aug 28, 2026View model evidence → | Sep 3, 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
Side-by-Side Facts
| Field | DeepSeek-V3.2 | GPT-5.3-Codex |
|---|---|---|
| Developer | DeepSeek | OpenAI |
| Family | Deepseek V3 2 | Gpt 5 3 |
| Model | DeepSeek-V3.2 | GPT-5.3-Codex |
| Version | DeepSeek-V3.2 | GPT-5.3-Codex |
| Lifecycle | active | active |
| Released | 2025-12-01 | Unknown |
| Knowledge cutoff | Unknown | 2025-08-31 |
| Input modalities | Text | Text, Image |
| Output modalities | Text | Text |
| Context window | 164K | 400K |
| Total parameters | 685.4B | Unknown |
| Active parameters | Unknown | Unknown |
| License | mit | Unknown |
| Open weights | Yes | No |
| API available | Yes | Yes |
| Self-hostable | Yes | No |
| Provider access | Deepinfra (Standard), Hugging Face (Standard), Openrouter (Standard) | Openai (Standard), Openrouter (Standard) |
| Capabilities | chat, generation, reasoning | chat, generation, reasoning, structured_outputs, tools |
DeepSeek V3.2 Capabilities
GPT-5.3 Codex Capabilities
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 MiniMax-M3MiniMax | vs | DeepSeek-V3.2DeepSeek | cross-developer peerstext |
DeepSeek-V3.2DeepSeek | vs |  GLM-5.3Z.ai | cross-developer peerstext |
DeepSeek-V3.2DeepSeek | vs |  Hy4 previewTencent | cross-developer peerstext |
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Primary Evidence
Sources and Freshness
Questions
DeepSeek V3.2 vs GPT-5.3 Codex FAQs
Is DeepSeek V3.2 or GPT-5.3 Codex better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both DeepSeek V3.2 and GPT-5.3 Codex, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, DeepSeek V3.2 or GPT-5.3 Codex?+
DeepSeek V3.2 is $0.26 and GPT-5.3 Codex is $1.75 per million tokens, so DeepSeek V3.2 is cheaper on this metric. DeepSeek V3.2 is $0.38 and GPT-5.3 Codex is $14.00 per million tokens, so DeepSeek V3.2 is cheaper on this metric.
Which has a larger context window, DeepSeek V3.2 or GPT-5.3 Codex?+
GPT-5.3 Codex has the larger sourced context window. DeepSeek V3.2 supports 164K and GPT-5.3 Codex supports 400K.
Which performs better in benchmarks, DeepSeek V3.2 or GPT-5.3 Codex?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can DeepSeek V3.2 or GPT-5.3 Codex be self-hosted?+
DeepSeek V3.2 is the only model in this pair currently marked as self-hostable. DeepSeek V3.2 is open weight; GPT-5.3 Codex is not marked open weight.
Can DeepSeek V3.2 and GPT-5.3 Codex understand images?+
DeepSeek V3.2 is not documented with image input; GPT-5.3 Codex is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, DeepSeek V3.2 or GPT-5.3 Codex?+
Neither has a larger sourced maximum output. DeepSeek V3.2 is — and GPT-5.3 Codex is 128K.
Do DeepSeek V3.2 and GPT-5.3 Codex support reasoning and tool use?+
DeepSeek V3.2: reasoning. GPT-5.3 Codex: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, DeepSeek V3.2 or GPT-5.3 Codex?+
DeepSeek V3.2 has 3 sourced provider routes; GPT-5.3 Codex has 2, so DeepSeek V3.2 has broader tracked availability.
Which offers better value, DeepSeek V3.2 or GPT-5.3 Codex?+
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.