There is no evidence-backed single best LLM for every agentic-coding task in 2026. Results depend on the model, the agent harness, the repository, the task, and how success is measured. The most useful choice is the model that completes your team’s representative work correctly, reliably, and at an acceptable total cost—not necessarily the one at the top of a leaderboard.
Available evidence offers useful candidates and evaluation lessons, but not a fair, same-harness ranking across all major providers. Treat published results as clues, then run a controlled bake-off on your own codebase.
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Which LLMs are worth testing for agentic coding?
Think of the names below as candidates to evaluate, not a ranked list of universal winners. Published results vary by workload and setup, and the available evidence does not support a direct, apples-to-apples comparison across every major model.
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OpenAI’s February 5, 2026 announcement says GPT-5.3-Codex reached a new high on SWE-Bench Pro and Terminal-Bench and reports strong results on OSWorld and GDPval. Those are the provider’s claims about its model and the announcement’s evaluation setup. They make GPT-5.3-Codex a reasonable candidate to test, particularly when your work resembles the evaluated tasks, but they are not independent evidence that it will outperform alternatives in your repository.
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OpenAI describes SWE-Bench Pro as spanning four languages. That is distinct from SWE-bench Verified, which OpenAI describes as Python-only.
Open-source options, including GLM 5.2
In a July 2026 internal study of real engineering tasks, Databricks found that its quality-for-cost frontier included models from OpenAI, Anthropic, and open source. The Databricks authors report that GLM 5.2 handled the highest task-difficulty level in that evaluation. This is a notable result from one large engineering organization, not proof that GLM 5.2 is best for every difficult task or codebase.
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Models from Anthropic and other providers
The Databricks study supports testing across provider and open-source options, but the evidence available here does not provide a harmonized, same-harness ranking of named models across providers. Choose specific current models available to your team and compare them under the same conditions rather than inferring an overall winner from a mixed leaderboard.
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The repository and task distribution matter
Databricks’ July 8, 2026 report describes an internal benchmark based on engineers’ coding tasks in a codebase of several million lines, covering Python, Go, TypeScript, and Scala. The company says the tasks and solutions were reviewed, while also noting that the exercise was not comprehensive. Its analysis found that about one quarter of observed coding interactions were tagged low-complexity and about 60% medium-complexity. Those shares describe the tasks Databricks analyzed; they are not universal proportions of software work.
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This kind of evidence is relevant because a model can perform well on isolated issue-and-test tasks yet struggle with a repository’s conventions, dependencies, build system, or cross-service changes. A result from one organization still cannot establish how a model will behave in yours.
The harness changes the outcome
An agent harness determines how a model receives context, uses tools, edits files, handles errors, and retries. Databricks reports that its harness choice dramatically affected both cost and quality, and that simple harnesses such as Pi often performed best on its workloads. That finding is specific to the company’s evaluation; it does not establish that a simple harness or Pi is always preferable.
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Token prices are not task costs
Databricks found token price to be a poor indicator of end-to-end task cost. A useful comparison includes the cost of repeated context, tool calls, retries, unsuccessful runs, and human intervention—not just the listed price per token. A cheaper call can still produce a more expensive completed task if it needs more attempts or review.
How to read coding-agent benchmarks
Benchmarks can help you shortlist candidates, but a score only means what its task set, model version, agent setup, and evaluation method allow it to mean.
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| Evidence | What it tells you | What it does not establish |
|---|---|---|
| SWE-bench Verified | The official SWE-bench documentation describes a human-validated subset of 500 SWE-bench instances and provides both a full leaderboard and a simplified bash-only comparison using mini-SWE-agent. | A rank alone does not predict your production results. The documentation cautions that release 1.x and 2.x results are not necessarily comparable: 2.x uses tool calling, while 1.x parses actions from model output. |
| SWE-bench and static issue benchmarks | These evaluate agents on repository issues: the agent receives a repository and issue description, edits files, and is assessed using tests. Human filtering for Verified was intended to address problems with some original tasks. | OpenAI’s 2025 explanation warns that public static GitHub tasks can be contaminated and cover only a narrow slice of autonomous software engineering. That is a reason to interpret scores carefully, not to dismiss every leaderboard result. |
| SWE-Bench Pro, Terminal-Bench, OSWorld, and GDPval | OpenAI’s February 2026 announcement reports GPT-5.3-Codex results across these evaluations. Terminal work and GUI interaction exercise capabilities that repository issue benchmarks may not capture. | Provider-reported results are not independent cross-provider comparisons. Do not combine scores from different evaluations or setups into a single ranking. |
| SWE-bench-Live | The project describes an automatically updating, multilingual, multi-OS task set. Its August 2026 note says maintainers began requiring rollout trajectories to verify submissions and check for information leakage. | The leaderboard page reported a loading error when reviewed, so no current live ranking can be substantiated here. |
| Vellum’s benchmark compilation | Vellum’s July 24, 2026 page compiles engineering-specific data from providers, Vellum, and the open-source community and can help identify metrics and candidates to inspect. | It does not establish a harmonized evaluation protocol across every model on the page. Check each result’s benchmark version, agent scaffold, date, and source. |
Training and workflow can also change results. Microsoft’s Agent Lightning repository reports that its training examples raised Qwen3.5-35B-A3B’s SWE-bench Verified score from 47.8% to 61.6% after training on 1.8K examples. This is a project-reported illustration of how training can affect a score, not a general comparison with frontier commercial models.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to choose a model for your codebase
Use a small, controlled evaluation that reflects the work you actually want the agent to do. Judge completed work, not model reputation or benchmark rank alone.
- Choose representative tasks. Use recent bug fixes, test work, and refactors with clear acceptance criteria. Include tasks in the languages, frameworks, and build environment that matter to your team.
- Set the conditions. Keep prompts, tools, context budget, permissions, and retry limits constant across candidates. Record the model and harness versions so the result is reproducible.
- Run candidates consistently. Give each model the same tasks and access. Repeat runs when task variability matters; a single success or failure can be misleading.
- Review the patches. Have a human assess whether the change meets the actual requirements, passes relevant tests, preserves unrelated behavior, and is maintainable.
- Record operational outcomes. Track task completion, elapsed time, total cost, tool errors, retries, and human interventions. Include failed runs and repeated context in cost accounting.
- Choose by workload. Prefer the candidate that delivers the best combination of correctness, repository fit, reliability, time, and cost for your team’s tasks.
What to include in a model comparison
- Task success and correctness: Did the patch meet acceptance criteria, pass the relevant tests, and avoid unrelated changes?
- Repository and language fit: Does the evaluation resemble your repository’s size, languages, frameworks, build tools, and conventions?
- Harness and tool reliability: Record the agent, shell or IDE tools, context management, permissions, and retry strategy. Benchmark documentation and the Databricks study both show why setup differences matter.
- Total cost and elapsed time: Count retries, repeated context, unsuccessful runs, and interventions alongside successful calls.
- Environment and task horizon: For terminal work, GUI interaction, or multiple operating systems, include tasks that exercise those environments rather than relying only on repository issue benchmarks.
- Evidence quality and recency: Note who ran the evaluation, when it ran, whether the tasks were public, and whether the model and harness versions match your setup.
So, which LLM is best for agentic coding in 2026?
The evidence supports a practical answer, not a universal champion: test strong candidates from commercial and open-source options in the harness and repository where they will be used. GPT-5.3-Codex has dated provider-reported results on several benchmarks; Databricks’ real-codebase study found viable options across providers and open source, including a high-difficulty result for GLM 5.2. Neither establishes a winner for your workload. Your controlled bake-off is the decision that can.
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