选型指南 · 2026-05-25 · by @zayuerweb-dev
Which Model to Use for AI Agents in 2026: Tool Calling, Long Tasks, Real Costs
Anyone building agents on top of LLMs hits this quickly: which model is "smarter" in a single chat turn has almost nothing to do with whether it can finish a thirty-step agent task without falling apart. Agents live or die on different things — whether tool calls keep their format, whether the model drifts off-goal over many steps, and how scary the token bill gets per task. This piece runs through the models worth using for agents in 2026 along those axes, and ends with a "what to route where" table. Prices and benchmarks are a May 2026 snapshot; check the official sites before you build.
30-second answer
- Default workhorse: Claude Sonnet 4.6 / Opus 4.7. The steadiest tool-calling format and the least likely to drift on long tasks. The default for agents.
- Most mature engineering: GPT-5.5. The fullest function-calling, structured-output (JSON schema), and SDK ecosystem. Fewest sharp edges.
- Long memory / lots of history: Gemini. Million-token context at the lowest input price, so you can keep all the history and observations in context.
- Cheap sub-tasks: DeepSeek, Qwen. Don't burn a frontier model on classification, summaries, and extraction steps.
- Don't pick on chat-leaderboard scores. Agents are won by "dozens of steps without breaking, at a cost you can live with," not by who answers one prompt best.
Compare these models' live prices and capabilities in the tool →
What agents actually need from a model
The core difference from chat: an agent has to decide, call a tool, read the result, and decide again — many steps, no human in the loop. So you judge a model on four things, not on single-turn answer quality:
- 1. Tool-calling reliability. Agents drive tools through function calling, and the model must emit valid argument formats every time. One broken JSON and the chain snaps. This is the first hard requirement.
- 2. Drift over many steps. Across dozens of steps a model can forget the goal, repeat work, or wander. Staying on-target and self-correcting is what decides whether the agent actually finishes.
- 3. Cost. One agent task is many turns, and each turn resends the history plus tool results. Tokens pile up fast — unit price times turns can be tens of times a single chat.
- 4. Context length. History, tool returns, and document chunks all go into context. The bigger it is, the more the agent can "remember" and the less state it loses.
Hold those four in mind and each model's trade-offs below make sense.
The five models, compared
- Claude Sonnet 4.6 / Opus 4.7 — the agent king. These are my default for agents. The most reliable tool-call format, and long agent runs of dozens of steps rarely go off the rails. Opus 4.7 hits 87.6% on SWE-bench Verified ("here's a real repo issue, can you fix it") and leads SWE-bench Pro at 64.3% — exactly the agentic-coding ability you want. Sonnet 4.6 is cheaper ($3/$15) and fine as the everyday workhorse; escalate to Opus for the hard tasks.
- GPT-5.5 — the most mature engineering. Not always the single-point best, but its function calling, structured output (strong JSON-schema constraints), parallel tool calls, SDK, and docs are the most complete, so it's the least painful to wire up. Strong on terminal-style tasks too (82.7% on Terminal-Bench 2.0). The cost is the highest token price ($5/$30), so put it where you truly need that engineering muscle.
- Gemini — cheap long memory. Million-token context plus the lowest input price ($1.25 input) lets you keep the whole history, tool returns, and documents in context without wincing. For agents that need long-term memory and carry a lot of state, it's the most cost-effective "memory store."
- DeepSeek R1 — a cheap reasoning sub-model. At $0.55/$2.19 with respectable reasoning and code, it's good for the "think a bit, not hard" steps inside an agent, at a fraction of a frontier model's cost.
- Qwen3 Max — Chinese and batch sub-tasks. Strong on Chinese and cheap ($1/$4). Hand it the high-frequency classification, extraction, and summary steps, and save your budget for the decision steps.
Cost: how many tokens one agent task really burns
This is the most underestimated part. An agent doesn't ask once and answer once — every step resends "system prompt + history + all tool returns." Say a task runs 20 steps, averaging 8,000 input and 500 output tokens per step as context accumulates:
- One task ≈ 160k input + 10k output tokens.
- All GPT-5.5: 160k × $5/1M + 10k × $30/1M ≈ $0.8 + $0.3 = $1.1 / task.
- All Claude Sonnet 4.6: ≈ $0.48 + $0.15 = $0.63 / task.
- Sonnet for the decisions, DeepSeek for classify/summarize sub-steps: down to under $0.3.
A dollar a task sounds trivial, but agents run at scale — a few thousand tasks a day and the gap is thousands of dollars a month. That's why in agent selection, routing per step beats "use the strongest everywhere." Prompt caching (cache the unchanging system prompt and history) saves a big chunk more on top.
Reading the benchmarks (τ-bench / SWE-bench / Terminal-Bench)
Chat leaderboards (like LMArena) are of limited use for agent selection. Look at the benchmarks built to test "can it use tools and finish a task":
- τ-bench / τ²-bench: tests calling tools by rules across multi-turn, customer-service-style tasks — the closest thing to a real agent. Claude models have led this kind of tool-use task for a while.
- SWE-bench Verified / Pro: real repo issues, can the agent fix them. The hard metric for agentic coding, led by Opus 4.7 (Verified 87.6% / Pro 64.3%).
- Terminal-Bench: multi-step work in a terminal, where GPT-5.5 stands out (82.7% on 2.0).
In short: agentic coding → SWE-bench, general tool use → τ-bench, terminal work → Terminal-Bench. Don't use single-turn chat scores as a proxy for agent ability. To compare capability dimensions side by side, use the comparison tool; for a fuller Opus 4.7 breakdown see this review.
Decision table and routing strategy
- Default workhorse (tool calls + long tasks): Claude Sonnet 4.6, escalate hard tasks to Opus 4.7
- Most mature function calling / structured output / terminal: GPT-5.5
- Long memory, lots of history, big background: Gemini (long context + cheap input)
- Cheap reasoning sub-steps: DeepSeek R1
- Chinese / batch sub-tasks (classify, extract, summarize): Qwen3 Max
The one rule that matters most in practice: don't bind one agent to a single model. Put model calls behind an interface and route by step difficulty and type — decisions to Claude/GPT, simple sub-steps to DeepSeek/Qwen, long background to Gemini. That controls cost and lets you switch instantly when a provider rate-limits or raises prices. The frontier models' single-point gaps are narrowing; the gap in engineering fit and cost structure is where your time actually pays off.
Related reading
FAQ
Which model should I use to build an AI agent? Default to Claude Sonnet 4.6 (steady tool calls, low drift on long tasks), escalate hard tasks to Opus 4.7. For the most mature function calling and structured output, GPT-5.5; for long memory and lots of history, Gemini; to save money, route simple sub-steps to DeepSeek / Qwen. Route per step rather than binding everything to one model.
Why can't I just use a chat leaderboard like LMArena? Chat leaderboards measure single-turn answer quality. Agents need multi-step tool calls that don't break, long tasks that don't drift, and controllable cost. τ-bench (tool use), SWE-bench (agentic coding), and Terminal-Bench (terminal work) are the relevant ones.
Why is agent cost so much higher than chat? Because every step resends the system prompt + full history + tool returns, so tokens accumulate with the step count. A 20-step task can be tens of times the tokens of a single chat turn. Prompt caching for the unchanging parts and routing simple sub-steps to cheaper models are the two main levers.
Can open models (DeepSeek, Qwen) be the main agent model? They can carry the cheap sub-steps, but for tool-calling reliability and long-task consistency, Claude / GPT are still safer today. The pragmatic move is to mix: closed frontier models for decisions, open models as the fallback for high-frequency simple steps.
Is longer context always better for agents? Long context lets an agent remember more history and observations and lose less state, but it's also more expensive and can "get lost in the middle" (information buried in a long context gets ignored). In practice, carry history as needed and summarize/compress, rather than stuffing everything in.