Find the right AI model, for every job
GPT-5, Claude, Gemini, GLM, Kimi, DeepSeek — the lineup keeps growing and they are not interchangeable. Whether you are coding, writing, researching, or just getting things done, this guide maps the frontier: side-by-side specs, real benchmarks, and plain-English guidance on which model fits which task.
Picking a model is a real decision — for anyone
Coder or writer, analyst or student, the same prompt can cost cents or dollars, fit or overflow, answer instantly or stall. Four axes decide most of it.
- Cost947×Price swings are enormous
Output token prices span 947× across the catalog. Pick wrong and a high-volume job costs an order of magnitude more than it should.
- Context10MContext windows differ wildly
From tight, cheap windows to 10M tokens. The right window depends on whether you feed in a paragraph or a whole book — or an entire codebase.
- Openness17Open vs. closed weights
17 tracked models ship open weights you can self-host for privacy and control — the rest are API-only. The trade-off shapes your setup.
- Latency250 tpsLatency is a feature
Throughput ranges from deliberate reasoners to models pushing 250 tokens/sec. A live back-and-forth and an overnight batch want opposite ends of that scale.
A cross-section of the frontier
Flagships, fast workhorses, and notable open-weight models — one card each. Open any model for full specs, benchmarks, and pricing.
Four questions get you 90% of the way
Most model decisions come down to the same handful of trade-offs. The guide walks each one with concrete examples.
- 01CapabilityMatch the model's strengths to the task — deep reasoning and agentic coding demand a frontier model; extraction and chat do not.
- 02CostEstimate tokens × price at your real volume. The cheapest model that clears your quality bar usually wins, often by a wide margin.
- 03ContextSize the window to the job. Whole-repo and long-document work needs room; most chat turns fit comfortably in a small, cheap window.
- 04Openness & privacyDecide if you need open weights to self-host for data control and predictable cost, or if a managed API is the pragmatic call.
Think you can call the right model?
The context quiz drops you into real, constrained scenarios — budget, latency, volume, privacy — and grades your pick against the trade-offs. Learn the reasoning, not just the answer.
You're launching a free consumer app that answers quick everyday questions — recipe swaps, plain-English explanations, trip ideas, unit conversions. It's plain text, the quality bar is 'feels smart enough,' replies must stream back instantly, and with millions of tiny queries a day the cost per answer has to be almost nothing.
- GPT-5.4 nanoGPT
- Gemini 3.1 Flash-LiteGemini
- DeepSeek-V3.2DeepSeek
- Claude Haiku 4.5Claude
- Models tracked
- 37 Models trackedacross 11 labs
- Output price / 1M
- $0.19–$180 Output price / 1Ma 947× spread
- Largest context
- 10M Largest contexttokens in one call
- Open-weight models
- 17 Open-weight modelsself-hostable
Every major lab, under one roof
11 labs across 3 countries — from frontier closed models to self-hostable open weights. We track them all, so you can weigh a Claude against a Kimi without opening sixteen tabs.
- AnthropicClaude · 6 models
- OpenAIGPT · 5 models
- Alibaba QwenQwen · 4 models
- DeepSeekDeepSeek · 4 models
- Google DeepMindGemini · 4 models
- Mistral AIMistral · 4 models
- Moonshot AIKimi · 3 models
- Meta AILlama · 2 models
- xAIGrok · 2 models
- Zhipu AIGLM · 2 models
- MiniMaxMiniMax · 1 model
Stop guessing. Start matching.
Compare any two models head-to-head, or browse the full catalog and filter to exactly what your task needs.