Gemini 3.1 Pro Preview Custom Tools vs GPT-5 Codex
A side-by-side comparison of two multimodal models — real specs, pricing, strengths and weaknesses, and a clear verdict on which to choose. Kept current by our agents.
Quick verdict: which should you choose?
Choose Gemini 3.1 Pro Preview Custom Tools if you need
- ✓massive context windows up to 1,048,756 tokens for very long mixed inputs
- ✓custom tools and strong cross-modal coherence on diverse data types
- ✓Google provider ecosystem with flexible tool extensions
- ✓handling of extremely large contexts without noted coherence loss
Choose GPT-5 Codex if you need
- ✓known high output speed of 149.9 t/s and lower price at $10/1M tokens
- ✓strong coding specialization and unified text-image reasoning
- ✓OpenAI provider with intelligence_index of 44.6
- ✓tasks limited to text and static images without tool configuration overhead
Verdict
GPT-5 Codex leads on known output speed (149.9 t/s), lower price ($10/1M vs $12/1M), and coding specialization, while Gemini 3.1 Pro Preview Custom Tools leads on context size (1,048,756 vs 400,000 tokens) and custom tool flexibility. Gemini's preview status introduces potential instability risks not noted for GPT-5 Codex. Neither model has a reported intelligence_index comparison or parameter count, leaving overall capability unclear from the given data.
Gemini 3.1 Pro Preview Custom Tools vs GPT-5 Codex: side by side
| Spec | Gemini 3.1 Pro Preview Custom Tools | GPT-5 Codex | Winner |
|---|---|---|---|
| Intelligence | — | 44.6 | Tie |
| Output speed | — | 150 t/s | Tie |
| Output price | $12.00/1M | $10.00/1M | GPT-5 Codex |
| Context | 1049K | 400K | Gemini 3.1 Pro Preview Custom Tools |
| Params | — | — | Tie |
| Type | Proprietary | Proprietary | Tie |
| Provider | OpenAI | Tie |
Detailed analysis
Context Window
Winner: Gemini 3.1 Pro Preview Custom ToolsGemini 3.1 Pro Preview Custom Tools provides a context of 1,048,756 tokens compared to GPT-5 Codex's 400,000. This gives Gemini a clear advantage for processing very large inputs. GPT-5 Codex notes high resource demands at maximum context while Gemini highlights effective large-window use.
Pricing
Winner: GPT-5 CodexGPT-5 Codex is priced at $10 per 1M tokens versus Gemini 3.1 Pro Preview Custom Tools at $12 per 1M. The $2 difference favors GPT-5 Codex for cost-sensitive workloads. Both are proprietary models from major providers.
Specialization & Tools
Winner: Gemini 3.1 Pro Preview Custom ToolsGemini 3.1 Pro Preview Custom Tools emphasizes flexible custom tools and cross-modal coherence. GPT-5 Codex focuses on coding specialization and unified text-image reasoning. Tool setup for Gemini requires additional configuration not mentioned for GPT-5 Codex.
Speed & Stability
Winner: GPT-5 CodexGPT-5 Codex reports a specific output speed of 149.9 t/s; Gemini 3.1 Pro Preview Custom Tools has no speed figure provided. Gemini's preview status may include occasional instability, a limitation not listed for GPT-5 Codex.
Gemini 3.1 Pro Preview Custom Tools
Pros
- +Strong cross-modal coherence on mixed inputs
- +Effective use of very large context windows
- +Flexible extension via custom tools
Cons
- –Preview status may include occasional instability
- –Large contexts can increase latency and cost
- –Tool setup requires additional configuration
GPT-5 Codex
Pros
- +Handles extremely large inputs effectively
- +Strong coding specialization
- +Unified text and image reasoning
Cons
- –High resource demands with maximum context
- –Limited to text and static images
- –Potential coherence loss in very long outputs
Summary: Gemini 3.1 Pro Preview Custom Tools vs GPT-5 Codex
Choose Gemini 3.1 Pro Preview Custom Tools for maximum context and custom tool needs. Select GPT-5 Codex when speed, lower cost, and coding performance are priorities. The data leaves intelligence and parameter comparisons unresolved.
Frequently asked questions
GPT-5 Codex shows advantages in speed and price while Gemini 3.1 Pro Preview Custom Tools leads in context size and tool flexibility; no single winner is clear without intelligence_index data.