GPT-5.2 wins on coding (85 vs 80). Gemini 3.1 Pro wins on price ($2 vs $12/1M input) and context window (2M vs 200K). For most workflows, GPT-5.2 is the stronger default — capable but outclassed — gpt-5.4 is now cheaper and better.
Pick GPT-5.2 for coding and research. Pick Gemini 3.1 Pro when research.
Capable but outclassed — GPT-5.4 is now cheaper and better.
Use GPT-5.2 if you want the strongest default. Switch only when cost, speed, or context length matters more than maximum reliability.
The shortest way to see the safest default, the lower-cost option, and the specialist pick before you read deeper.
GPT-5.2 is the safest overall answer here when you want the strongest default instead of the lowest list price.
Grok 4 is the lower-cost option to start with when you still need useful output at scale.
Gemini 3.1 Pro is the better pick when response speed matters more than maximum reasoning depth.
GPT-5.2 leads on coding with a score of 85 vs 80 for Gemini 3.1 Pro.
Gemini 3.1 Pro has the larger context window: 2M vs 200K for GPT-5.2.
Gemini 3.1 Pro is cheaper at $2/1M input tokens vs $12/1M for GPT-5.2.
Choose GPT-5.2 for coding and research — serious coding and complex product work.
Choose Gemini 3.1 Pro when research.
Gemini 3.1 Pro is the more cost-efficient option at $2/1M — worth considering if token volume is a concern.
This comparison focuses on the models most likely to answer this search intent well, not every model in the directory.
Capable but outclassed — GPT-5.4 is now cheaper and better.
Best for research and deep document analysis — 2M context at the best premium price.
Use these cards as the practical decision layer: what each leading option is good at, and when it becomes the wrong default.
Capable but outclassed — GPT-5.4 is now cheaper and better.
Serious coding and complex product work
You're starting a new project — GPT-5.4 is cheaper and more capable.
Best for research and deep document analysis — 2M context at the best premium price.
Research, deep document analysis, and long-context reasoning at competitive pricing
Your primary use case is writing quality or agentic coding — Claude wins both.
UseRightAI recommendations are based on practical decision factors people actually feel in day-to-day use.
Benchmark scores from SWE-bench (coding), ARC-AGI-2 (reasoning), and MMLU (knowledge breadth) — cross-referenced against Chatbot Arena community votes to filter out cherry-picked provider claims.
Input and output costs verified directly against each provider's official API pricing page. Updated whenever a price change is detected. Value-per-dollar is weighted separately from raw benchmark rank.
Advertised context sizes are noted but scored against real-world usability — models that degrade significantly at large contexts are penalised even if the window is technically available.
Production signals matter more than lab scores. We weight Cursor and Windsurf defaults, HackerNews sentiment, developer surveys, and which models teams actually keep using after the honeymoon period.
One-off wins on cherry-picked benchmarks don't move our rankings. We favour models that stay dependable across repeated prompts, diverse task types, and long sessions without degrading.
Time-to-first-token and output throughput from Artificial Analysis speed benchmarks. Latency is categorised from Very fast to Deliberate — relevant when building interactive or high-throughput products.
Data sources
The fastest way to see where the recommendation shifts when your priority changes.
Capable but outclassed — GPT-5.4 is now cheaper and better.
Best for research and deep document analysis — 2M context at the best premium price.
Reliable at debugging and multi-file code edits
Strong structured reasoning for product and technical workflows
Solid default for teams that want one premium OpenAI model
Superseded by GPT-5.4 for most use cases
Claude Sonnet 4.6 leads on both coding and writing quality
Newsletter
Useful if you care about ranking shifts, pricing changes, or a better recommendation appearing in this decision path.
No spam. Useful updates only. Affiliate disclosures always clearly labeled.
GPT-5.2 wins on more categories — coding, research, reasoning. Gemini 3.1 Pro is the better pick when research. The right choice depends on your specific use case.
Gemini 3.1 Pro is cheaper at $2/1M input and $12/1M output. GPT-5.2 costs $12/1M input and $38/1M output.
Gemini 3.1 Pro has the larger context window at 2M tokens vs GPT-5.2's 200K. For large document analysis, Gemini 3.1 Pro is the stronger pick.
GPT-5.2 is better for coding with a score of 85 vs Gemini 3.1 Pro's 80. For the highest coding quality available, Claude Sonnet 4.6 (79.6% SWE-bench) or Opus 4.6 (80.8%) remain benchmarks.
Both GPT-5.2 and Gemini 3.1 Pro have similar speed profiles — rated balanced.
