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Which AI model do you use for what? Not the leaderboard, but what works in practice: reliability on agentic work, long context, tool and MCP steering, speed and cost.
Read this first: the classic benchmarks are saturated. On SWE-bench Verified the top models now sit within a few points of each other (the independent Vals AI list was archived in September 2026 because it no longer separated the leaders). Chasing that last point is pointless, choose based on your workflow and test on your own project.
The three tiers
- Frontier (closed): the hard 20%, with complex refactors, autonomous agents and difficult debugging. Claude Opus (Anthropic), GPT-5 generation (OpenAI), Gemini 3 generation (Google).
- Cheap & good: everyday work for a fraction of the price. Claude Sonnet, Gemini Flash, DeepSeek V-series Flash, MiniMax M-series, Grok.
- Open-weight: self-hosting, no vendor lock-in. Qwen3 Coder, Kimi (K2/K3), GLM-5, DeepSeek V-series, Llama.
A pattern that is common in 2026: most of your agent traffic on an open-weight or cheap model, and only the hardest tasks escalated to a frontier API.
Model families at a glance
| Family | Provider, weights and access |
|---|---|
| Claude Opus | Anthropic, closed. Best agentic coding, long refactors, hard reasoning. Via API, Claude apps, Cursor, Cline. |
| Claude Sonnet / Haiku | Anthropic, closed. Everyday production work and many small tasks. Via API, Claude apps, IDE agents. |
| GPT-5 generation | OpenAI, closed. A balanced all-rounder with a mature tool ecosystem. Via API, ChatGPT, Copilot, Cursor. |
| Gemini 3 Pro / Flash | Google DeepMind, closed. Very large context (whole codebases), multimodal and fast. Via API, AI Studio, Vertex. |
| DeepSeek V-series | DeepSeek, open weights (MIT). Near-frontier coding at low cost. Via API, OpenRouter or locally on heavy hardware. |
| Qwen3 Coder | Alibaba / Qwen, open weights (Apache-2.0). The default open-weight coding model, in a wide range of sizes. Locally (Ollama, LM Studio) or via API. |
| Kimi (K2 / K3) | Moonshot AI, open weights (adapted MIT). Agentic coding and long-context question answering. Via API, OpenRouter or self-hosted. |
| GLM-5 | Zhipu AI, open weights. GPT-class quality in the open. Via API or self-hosted. |
| MiniMax M-series | MiniMax, open weights. Cost-conscious agent loops. Via API or self-hosted. |
| Llama | Meta, open weights. Self-hosting, very large context and a broad ecosystem. Locally or via API. |
Exact version numbers change monthly, so treat the families above as the stable layer and check the current releases before you standardise.
Short advice
- Hardest tasks and highest reliability: a frontier model from the Claude Opus class.
- Best price/quality from a frontier lab: the Gemini Flash class.
- Best open-weight (self-hosted): Qwen3 Coder for flexibility; Kimi or GLM-5 for quality close to frontier.
- Cheapest workable agent loops: the MiniMax M-series and DeepSeek Flash-class APIs.
- Huge codebases in one go: the Gemini Pro class (million-token context).
What matters more than the leaderboard
- The harness. Cursor, Cline, Aider, Claude Code and your own scripts get very different results out of the same model. The agent loop often weighs more than the model.
- Tool and MCP steering. For vibecadding, whether a model can reliably steer your CAD through MCP (sketches, constraints, features) matters more than a few benchmark points.
- Cost per task, not per token: a cheap model that needs five attempts can end up more expensive than a frontier model that gets it right in one.
- Context window and how well the model uses it.
- Local versus API: self-hosted open weights mean no cost per seat and full data sovereignty, but only models under roughly 120B fit on hardware you own yourself, and the trillion-parameter open models are “open” through an API, not on your desk.
Check the current figures
Because scores shift weekly, you are better off verifying yourself before you standardise:
- swebench.com, the official SWE-bench leaderboards
- Vals AI, one standardised harness (archived September 2026, saturated)
- Aider Polyglot leaderboard, code editing in multiple languages
- LMArena, human preference (Elo ranking)
Last updated: November 2026.