Anthropic's API is the strongest default for coding agents: the best tool-use reliability and long-context behaviour in agentic loops. OpenAI is the closest competitor with the broadest ecosystem, Google Gemini has the largest context window, OpenRouter gives you every model behind one API, and DeepSeek is dramatically cheaper.
Editorial judgement last reviewedBuilding a coding agent puts different demands on a model than a chat product does. The model isn't producing text for a person to read, it's calling tools in a loop, reading their output, and deciding what to do next, dozens of times, without a human correcting it between steps.
That makes reliability compound. A model that formats a tool call correctly 98% of the time fails roughly once every fifty calls, which on a thirty-step agentic task means most tasks contain a failure. The difference between 98% and 99.5% is not a small difference in an agent loop; it's the difference between a product and a demo.
Our LLM API comparison covers eighteen providers on context window, tool use, pricing per million tokens, caching, latency and rate limits.
We ranked for agentic coding specifically, not for benchmark scores.
Tool-use reliability. Whether the model produces well-formed tool calls consistently under pressure, recovers from tool errors, and doesn't hallucinate parameters. This matters more than raw coding ability.
Long-context behaviour. Not the advertised window but whether the model still uses information from early in a long conversation. Agent loops accumulate context fast.
Prompt caching, which is the single biggest cost lever in agentic workloads because the system prompt and file contents get resent on every step.
Cost per completed task, not per token. A cheaper model that needs twice the steps isn't cheaper.
Rate limits and latency, because an agent making thirty sequential calls feels every second of per-call latency.
The most reliable tool use in the category, which is the property that matters most when a model is calling functions in a loop with no human between steps. Claude models are consistently well-behaved about staying inside a schema, recovering when a tool returns an error, and not inventing parameters.
Long-context behaviour is strong: information from early in a long agent session actually gets used, rather than being nominally in the window and functionally forgotten.
Prompt caching is well implemented and is the single biggest cost lever for agents, the system prompt and file contents are resent every step, and caching them changes the economics substantially.
Not the cheapest per token, and the model family is the one to beat rather than the one to undercut on price. Measured per completed task, it's competitive with anything here.
Excellent models, mature function-calling, structured outputs with schema enforcement, and by far the largest ecosystem, every framework, every library and every tutorial supports it first.
Structured output enforcement is genuinely useful for agents: constraining generation to a schema removes a whole category of parsing failure that you'd otherwise handle with retries.
Reasoning-capable models add a different trade-off for agents: better planning on hard tasks, more latency and cost per step. Worth using selectively for the planning step rather than uniformly across a loop.
If your organisation is already standardised on OpenAI
for procurement or data-handling reasons, there's no strong argument to move.
The biggest usable context windows available, which matters for agents that need to hold a large amount of a codebase in view at once rather than searching for it incrementally. For some architectures that changes the design, you can put substantially more of a repository in the prompt and skip a retrieval layer.
Competitive pricing, particularly at the smaller model sizes, and strong multimodal capability if your agent needs to read screenshots or diagrams.
Tool-use reliability has improved a lot but is worth testing against your own loop rather than assuming parity with the leaders. The ecosystem integrations are also thinner, so budget more glue code.
One API in front of essentially every model worth using, with automatic fallback when a provider has an outage and a single bill across all of them. For a product that wants to route different steps to different models, a cheap model for classification, a strong one for planning, this removes a lot of integration work.
It's also the fastest way to evaluate. Swap a model identifier and rerun your agent against a new model without touching your integration, which turns model selection into an experiment rather than a project.
You're adding a hop, which costs a little latency and puts a third party in the path of your traffic. Check the data-handling terms if that matters to you.
Dramatically cheaper per token than the frontier labs while remaining genuinely capable at coding tasks. For high-volume agentic workloads where cost is the binding constraint, the difference is large enough to change what's economically viable.
The trade is tool-use reliability under pressure, which is where the frontier models earn their premium. In an agent loop those failures compound, so test against your own harness with your own tools rather than trusting benchmarks.
A sensible pattern is mixed routing: a cheap model for the mechanical steps, reading files, summarising, classifying, and a frontier model for planning and for anything where a mistake is expensive.
Also check data-handling and jurisdiction terms against your own requirements before sending customer code.
An agent loop resends an enormous amount of identical context on every step. The system prompt, the tool definitions, the project instructions, the files already read, all of it goes back in on step two, step three and step thirty.
Without caching, you pay full input price for that repetition every time, and it dominates your bill completely. With caching, the repeated prefix costs a fraction, and the economics of long agent sessions change from uncomfortable to routine.
Getting value from it requires structuring the prompt correctly: stable content first, system prompt, tool definitions, project context, and volatile content last. If something changes near the start of your prompt on every step, the cache never hits and you've built the expensive version by accident.
Check three things per provider: whether caching exists, what the minimum cacheable prefix length is, and how long entries live. A five-minute cache lifetime is fine for a continuous agent loop and useless for a user who wanders off mid-task.
This single optimisation typically matters more to your bill than which model you chose, which is why the pricing comparison on our LLM hub records cache read and write rates separately.
Public coding benchmarks measure whether a model can produce a correct patch given a well-formed problem. That's useful and it is not what an agent does.
What an agent needs is: producing tool calls that match your schema, every time; handling a tool that returns an error without giving up or hallucinating success; not inventing a tool that doesn't exist; stopping when the task is done rather than continuing to poke at it; and staying coherent after twenty steps of accumulated context.
None of that shows up in a benchmark number, and models that score similarly can behave very differently in a loop.
The practical evaluation is to build a small harness with your actual tools, define twenty representative tasks, and run each candidate model ten times. Measure completion rate, average steps to completion, and total cost. That takes a day and tells you more than any leaderboard.
Pay particular attention to failure behaviour. A model that fails loudly and stops is far easier to build a product around than one that confidently reports success on a task it didn't complete, and that distinction is invisible in aggregate scores.
No. Benchmarks measure patch generation on well-formed problems; agents need tool-call reliability, error recovery and coherence over long loops. Models with similar scores often behave very differently in an agentic harness.
Far less than you'd fear with prompt caching configured properly, and far more than you'd like without it. Caching the stable prefix is the dominant cost lever, usually a bigger factor than which model you picked.
Yes, and it's the standard optimisation. Route mechanical steps, reading, summarising, classifying, to a cheap model and planning or high-stakes edits to a frontier one. OpenRouter
makes that routing straightforward.
Yes, and the roster includes a self-hosted vLLM baseline for comparison. Open-weight models have improved substantially, though tool-use reliability in long loops still lags the frontier. It's the right answer when code cannot leave your infrastructure.
