Compare AI Models on CometAPI

For software engineering tasks, the top performers cluster around a few families. Claude (Opus/Sonnet tiers) and Grok both lead SWE-bench evaluations, and Claude powers the two most widely adopted AI coding editors on the market. Claude excels at rapid prototyping and agentic terminal workflows, while Gemini CLI has an edge for large-context refactors thanks to its longer context window. For budget-conscious teams running high volume, GLM (the open-weight series from Z.ai) reaches a high fraction of frontier coding performance at a dramatically lower price point. Bottom line: For raw benchmark performance, Claude Opus/Sonnet and Grok are the current leaders. For cost-optimized coding at scale, DeepSeek V3 and GLM are compelling alternatives.

Speed depends on what you're measuring — throughput (tokens per second) and latency (time to first token) often favor different model families. "Mini" and "Flash" tier models consistently win on both TTFT and throughput for chat-style workloads, while reasoning-focused tiers are inherently slower because they generate more internal thinking tokens before responding. Among current options, compact open-source families like IBM Granite lead raw throughput on the leaderboard, while Flash-Lite variants from Google are among the fastest closed-source options. For proprietary APIs, the "Mini," "Fast," and "Haiku" sub-tiers from OpenAI, xAI, Anthropic, and Google each offer near-frontier quality at a fraction of the latency of their flagship counterparts. Bottom line: If latency is your primary constraint, compare the "Flash," "Mini," or "Haiku" variants of each provider family — they're purpose-built for speed-sensitive, high-frequency workloads.

Pricing follows a clear tier structure across providers. DeepSeek V3 remains one of the most aggressively priced options for frontier-adjacent reasoning, while Google's Flash-Lite family and OpenAI's Mini tier both sit in the sub-$0.50/million-input-token range. For scale deployments with long contexts, Gemini Flash-Lite offers a 1-million-token context window at one of the lowest per-token rates among closed-source options, making it particularly attractive for document-heavy pipelines. Open-weight models like Qwen and Llama — self-hosted — eliminate per-token costs entirely, at the expense of infrastructure overhead. Bottom line: The cheapest model depends on your token ratio (input-heavy vs. output-heavy) and context length requirements.

Vision capability is now standard across all major frontier families, but the implementations differ meaningfully. Gemini was trained natively on image-text pairs from the ground up, giving it a structural advantage in multimodal understanding — particularly for video and multi-image tasks. GPT leads on broad multimodal benchmarks, while Claude offers strong practical performance on code screenshots and technical diagrams. DeepSeek's primary V3 series is text-only; its separate VL family handles vision tasks. For open-weight options, Qwen VL rivals top-tier proprietary models across document comprehension, OCR in 32+ languages, and GUI-based computer use tasks. Bottom line: GPT, Claude (Sonnet and above), Gemini (all tiers), and Qwen VL all support image input today. If your workflow involves video frames, multi-image comparison, or very high image volume, Gemini's native multimodal architecture and lower per-image cost give it a practical edge.