Gemini 3.8 Flash Cyber vs GPT-5.3 Codex
At a Glance
| Compare |  Gemini 3.8 Flash CyberGoogle DeepMind |  GPT-5.3 CodexOpenAI |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | Not reported | $1.75Openai ↗ · Sep 3, 2026 |
| Output priceFrom · USD / 1M tokens | Not reported | $14.00Openai ↗ · Sep 3, 2026 |
| Context windowMaximum documented tokens | Not reported | 400K |
| Model facts checked | Sep 2, 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 | Gemini 3.8 Flash Cyber | GPT-5.3-Codex |
|---|---|---|
| Developer | Google DeepMind | OpenAI |
| Family | Gemini 3 | Gpt 5 3 |
| Model | Gemini 3.8 Flash Cyber | GPT-5.3-Codex |
| Version | Gemini 3.8 Flash Cyber | GPT-5.3-Codex |
| Lifecycle | active | active |
| Released | 2026-09-02 | Unknown |
| Knowledge cutoff | Unknown | 2025-08-31 |
| Input modalities | Text | Text, Image |
| Output modalities | Text | Text |
| Context window | Unknown | 400K |
| Total parameters | Unknown | Unknown |
| Active parameters | Unknown | Unknown |
| License | Unknown | Unknown |
| Open weights | No | No |
| API available | No | Yes |
| Self-hostable | No | No |
| Provider access | Unknown | Openai (Standard), Openrouter (Standard) |
| Capabilities | automated-patching, cybersecurity, reasoning, vulnerability-detection | chat, generation, reasoning, structured_outputs, tools |
Gemini 3.8 Flash Cyber Capabilities
GPT-5.3 Codex Capabilities
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Gemini 3.8 Flash CyberGoogle DeepMind | vs |  GLM-5.3Z.ai | cross-developer peerstext |
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Primary Evidence
Sources and Freshness
Questions
Gemini 3.8 Flash Cyber vs GPT-5.3 Codex FAQs
Is Gemini 3.8 Flash Cyber or GPT-5.3 Codex better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Gemini 3.8 Flash Cyber and GPT-5.3 Codex, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Gemini 3.8 Flash Cyber or GPT-5.3 Codex?+
Only GPT-5.3 Codex has a directly sourced input price: $1.75 per million tokens. Only GPT-5.3 Codex has a directly sourced output price: $14.00 per million tokens.
Which has a larger context window, Gemini 3.8 Flash Cyber or GPT-5.3 Codex?+
Neither model has a larger sourced context window in this comparison. Gemini 3.8 Flash Cyber is — and GPT-5.3 Codex is 400K.
Which performs better in benchmarks, Gemini 3.8 Flash Cyber 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 Gemini 3.8 Flash Cyber or GPT-5.3 Codex be self-hosted?+
Both models have the same recorded self-hosting status: unsupported. Gemini 3.8 Flash Cyber is not marked open weight; GPT-5.3 Codex is not marked open weight.
Can Gemini 3.8 Flash Cyber and GPT-5.3 Codex understand images?+
Gemini 3.8 Flash Cyber 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, Gemini 3.8 Flash Cyber or GPT-5.3 Codex?+
Neither has a larger sourced maximum output. Gemini 3.8 Flash Cyber is — and GPT-5.3 Codex is 128K.
Do Gemini 3.8 Flash Cyber and GPT-5.3 Codex support reasoning and tool use?+
Gemini 3.8 Flash Cyber: 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, Gemini 3.8 Flash Cyber or GPT-5.3 Codex?+
Gemini 3.8 Flash Cyber has 0 sourced provider routes; GPT-5.3 Codex has 2, so GPT-5.3 Codex has broader tracked availability.
Which offers better value, Gemini 3.8 Flash Cyber 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.