Stop trusting static leaderboard screenshots. Compare Claude, GPT, Gemini, and Chinese models (DeepSeek, Kimi, Qwen, GLM) on task fit, weaknesses, and real blended API costs.
Multi-file engineering accuracy and human-preference writing quality. Holds top position on blind human evaluations. Best for multi-file refactors and resolving ambiguous bug reports.
Price. Sits at the top of the cost range. Standard context window is large, but default window is smaller than Gemini's massive baseline.
Versatility and autonomous agent tooling. The default for creative range, long background jobs, and CLI-driven coding agents running semi-independently.
Priced at or above Claude on most rates, without a clear specialist edge. Reasoning benchmark advantages live in dedicated reasoning lines rather than mainline GPT.
Price-performance and native multimodality. Cheapest Western flagship (~half input/output cost of Claude/GPT). Largest standard context window and best on dense video/audio inputs.
Voice. Struggles to produce natural, less "obviously AI" long-form writing. Performs best under structural stress rather than everyday Q&A.
Price-to-capability by a huge margin. DeepSeek's flagship coding tier lands within points of Claude on SWE-bench Verified at 1/10th the cost. Permissive open-weight licenses.
English nuance (occasional ESL-pattern artifacts in long text). Compliance restrictions and data provenance make legal approval harder in regulated sectors.
Click your current engineering situation below to identify the ideal model match, why it fits, and which alternative options are viable.
Calculates the cost of a daily workload (based on a realistic split of 70% input tokens and 30% output tokens) using published 2026 rates.
Adjust from 1M to 100M tokens per day.
Standard split for coding loops where codebases are ingested as context inputs.
Blended rate: ~$2/$12 per million tokens.
Blended rate: ~$5/$25â30 per million tokens.
Blended rate: ~$0.40â$0.90 per million blended.