Best LLMs for Coding in 2026: How to Compare Them

There is no single coding model that is best for every repository or workflow. A model that performs well on a benchmark may still struggle with your language, framework, test setup, or agent tools. Choose the evaluation that resembles your work, then test shortlisted models on a fixed set of tasks from your own codebase.

Choose the benchmark for the job

SWE-bench Verified is a human-validated subset of 500 SWE-bench issue instances. It tests whether a system can resolve selected GitHub issues under the benchmark’s specified setup. The maintainers publish both a broad leaderboard of agent systems and a more controlled bash-only model evaluation; check which one a score comes from and report the agent version.

Terminal-Bench pairs a task dataset with an execution harness for agents in a terminal environment. Tasks are checked by test scripts, and the harness runs in a sandbox. It can reveal how a system handles shell interaction and multi-step tool use, but a terminal score is not the same as a repository issue-resolution score. See the Terminal-Bench project for its current versions and setup.

DeepSWE is a separate benchmark from SWE-bench Verified. Never substitute a DeepSWE score for a SWE-bench Verified result. For all of these evaluations, results can shift with the harness, tools, model snapshot, attempt budget, and scoring rules. See our SWE-bench guide and benchmark glossary for context.

A useful comparison process

Use the same issue set, starting repository state, time limit, tools, and retry policy for every model. Record whether the harness can browse the web, install packages, run tests, and edit files. Then review not only whether a task passed, but also the correctness of the patch, test coverage, time to completion, and how much supervision it required.

A provider or benchmark result is evidence about that evaluation, not a guarantee for your project. If scores come from different harnesses or versions, treat them as separate observations instead of ranking them on one table.

Estimate cost from actual usage

Per-token prices alone do not tell you the cost of a completed coding task. Input can include system instructions, repository context, tool output, and retries; output includes plans, code, and explanations. Cache rules and model-specific pricing also differ.

Use your own task traces to calculate a budget:

If provider rates are quoted per million tokens, calculate each category separately:

task_cost = (uncached_input_tokens / 1,000,000 * uncached_rate_per_1M)
          + (cached_input_tokens / 1,000,000 * cached_rate_per_1M)
          + (output_tokens / 1,000,000 * output_rate_per_1M)

Measure the number of successful tasks as well as dollars spent. If you estimate before collecting traces, state the assumed token counts and label the result as an estimate. Once you have real runs, calculate cost from actual input, cache hits, output, retries, and successful tasks.

Check provider pricing at the time you deploy. For example, Z.ai currently lists GLM-5.3-Flash at $0.15 per million uncached input tokens, $0.03 per million cached input tokens, and $0.50 per million output tokens; DeepSeek’s current Flash API rates vary between peak and off-peak periods. Prices and aliases may change.

Shortlist by workflow, then verify

For interactive pair programming, weigh response time, instruction following, and the quality of small edits. For autonomous issue resolution, weigh end-to-end success, safe tool use, and recovery from failed commands. For code review, test whether the model can cite relevant files and distinguish confirmed defects from guesses.

Check licensing before self-hosting or embedding a model in a product. Open weights do not imply that every use is allowed, and hosted APIs have different data-handling terms. If local execution matters, include checkpoint size, quantization, KV-cache needs, runtime support, and operational effort in the decision.

Use the RankLLMs leaderboard as a discovery aid, then inspect each model’s scorecard and its cited data. Our open-weight LLM guide covers licensing, deployment, and cost factors in more detail.