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There is no evidence-backed single best LLM for every agentic-coding job in 2026. Real results depend on the model, the agent harness, the repository, the task and the way success is measured. A useful starting point is to shortlist capable models, then compare them on representative work from your own codebase. Public benchmarks can help you decide what to test; they cannot guarantee how a model will perform in your environment.
Which LLM should you choose for agentic coding?
Choose the model that completes your team’s work correctly and reliably at an acceptable total cost—not the one with the highest isolated benchmark score or lowest token price. “Agentic coding” includes more than generating a patch: an agent may need to inspect a repository, plan changes, use tools, run tests, diagnose failures and revise its work. A model that looks strong in one setup can behave differently with another harness, tool policy or codebase.
The most relevant real-codebase evidence here is Databricks’ July 8, 2026 report on coding tasks from its engineers. Its internal evaluation covered a multi-million-line codebase and Python, Go, TypeScript and Scala. The Databricks authors reported strong options from OpenAI, Anthropic and open-source models; GLM 5.2 handled the highest task-difficulty level in their evaluation. They also found that the harness substantially affected quality and cost, and that token price was a poor guide to end-to-end task cost. These are findings from one organization’s workloads, not a universal ranking.
That makes the practical answer conditional: shortlist models that support your agent setup, then run a controlled bake-off on your own tasks. Treat published results as clues about which candidates and capabilities to investigate, not as a substitute for that evaluation.
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What the available evidence can—and cannot—tell you
Real-codebase results are useful, but local to the organization
Databricks’ study is valuable because it reflects engineering work in a large, real repository rather than only a public benchmark. But the authors describe it as non-comprehensive. Its workload, task mix, harness and cost setup may differ from yours. In the same report, roughly one quarter of the coding interactions analyzed were tagged low complexity and about 60% medium complexity. Those approximate shares describe Databricks’ analysis, not the proportion of easy or medium work every team should expect.
The report also found that simpler harnesses, including Pi, often performed best on its workloads. That is a reason to test harness complexity rather than assume more orchestration is better—not a general recommendation that Pi, or any particular model-harness pairing, will win in your environment.
Published benchmark results measure particular setups
SWE-bench evaluates agents on repository issues: the agent receives a repository and issue description, makes changes, and is evaluated using tests. The SWE-bench team describes SWE-bench Verified as a human-validated subset of 500 instances. Human review addresses problems found in some original tasks, but a benchmark still represents a limited sample of software work.
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The official SWE-bench Verified page distinguishes its full leaderboard from a simplified bash-only comparison using mini-SWE-agent. It also cautions that results from release 1.x and 2.x are not necessarily comparable: 2.x uses tool calling, while 1.x parses actions from model output. Before comparing scores, check the benchmark release, agent configuration and evaluation method.
OpenAI’s 2025 explanation of SWE-bench notes that public, static GitHub tasks can be vulnerable to contamination and cover only a narrow distribution of autonomous software-engineering work. These are reasons to interpret leaderboard results carefully, not grounds to dismiss all benchmark data.
Provider announcements are claims about the provider’s evaluation
In its February 5, 2026 announcement, OpenAI said GPT-5.3-Codex reached a new high on SWE-Bench Pro and Terminal-Bench and reported results on OSWorld and GDPval. These are OpenAI’s claims about its model and evaluation setup. They can identify a model worth testing, but they are not an independent, same-conditions comparison against every other provider.
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OpenAI says SWE-Bench Pro spans four languages; its announcement describes SWE-bench Verified as Python-only. Keep those benchmark scopes distinct when deciding whether a result resembles a multilingual codebase. A score on one benchmark should not be combined with a score from another setup to create an overall ranking.
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Vellum’s July 24, 2026 engineering benchmark page compiles results from providers, Vellum and the open-source community. It can help surface candidates and metrics, but it does not establish one harmonized protocol for every model listed. For any result you use, record who ran it, the benchmark version, the agent scaffold and the date.
SWE-bench-Live describes itself as an automatically updating, multilingual and multi-OS task set. In an August 2026 note, it said submissions began requiring rollout trajectories so maintainers could verify submissions and check for information leakage. Its leaderboard did not load when reviewed, so no current live ranking can be substantiated here.
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Agent training and workflow can change scores
Microsoft’s Agent Lightning repository reports that its training examples improved Qwen3.5-35B-A3B on SWE-bench Verified from 47.8% to 61.6% after training on 1.8K examples. This project-reported result illustrates that training and agent workflow can influence a score. It is not a general head-to-head comparison with frontier commercial models.
How to compare models for your codebase
Evaluate candidates against the work you actually expect an agent to do. Score more than whether a patch passes one test: a useful comparison includes correctness, repository fit, tool reliability, human intervention, time and full run cost.
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| What to compare | What to record | Why it matters |
|---|---|---|
| Task success and correctness | Whether acceptance criteria are met, relevant tests pass, and unrelated behavior remains intact; note reviewer corrections. | A plausible patch or a passing narrow test is not by itself proof that the task was completed correctly. |
| Repository and language fit | Repository size and structure, languages, frameworks, build tools, tests and conventions represented in each task. | A result from a different codebase may not predict performance on a multi-language repository or one with different tooling. |
| Harness and tool reliability | Agent and version, tools, shell or IDE access, context management, permissions, retry policy, tool failures and human interventions. | Databricks found that its harness choice materially affected quality and cost; SWE-bench results can also vary with agent release and configuration. |
| Total cost and elapsed time | Cost per completed task, including failed runs, retries and repeated context; elapsed time to an acceptable result. | Token price alone does not capture how much work the agent needs to finish the task. |
| Environment and task horizon | Whether work needs terminal use, GUI interaction, multiple operating systems, or several steps across a longer task. | Different benchmarks test different capabilities. Match the test to the environment instead of treating one score as a measure of every agent skill. |
| Evidence quality and recency | Who ran the evaluation, when, on which model and harness versions, and whether tasks were public or drawn from an internal workload. | Results are easier to interpret when their provenance and setup resemble the decision you need to make. |
Run a fair in-house bake-off
A small, repeatable evaluation on representative work is more useful than a large pile of mismatched public scores. The following protocol is a practical recommendation based on the limitations of published benchmarks and the Databricks evaluation; it is not a published, validated standard.
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- Choose representative tasks. Select recent work with clear acceptance criteria: for example, bug fixes, test additions and refactors. Include tasks in the languages, frameworks and build environment your team actually uses. Avoid selecting only tasks that are unusually easy to specify or easy to test.
- Define success before running models. Write down what must change, which tests or checks apply, and what behavior must not regress. Use the same criteria to review every candidate.
- Hold the setup constant. Keep prompts, tools, context budget, permissions and retry limits the same. Record the model and harness versions. If a model requires a different setup, treat that as a separate configuration rather than silently changing the rules mid-comparison.
- Run candidates consistently. Apply the same task set to each candidate. Repeat runs when variability between attempts matters, and preserve run logs so tool failures and interventions are visible.
- Have a human review the result. Check correctness and maintainability, not just whether the agent says it is done or a single automated test passes. Apply the same review standard across candidates.
- Track the complete outcome. For each task, record completion against the acceptance criteria, elapsed time, total cost, tool errors, retries and human interventions. Compare cost and time per acceptable completed task, not just cost per attempt.
- Make the choice by workload. If candidates trade off speed, reliability, cost or performance by language, decide which trade-off matters for your tasks. A model that wins on one kind of work need not be the best default for every task.
Choose benchmarks that match the job
Use public benchmarks to narrow a shortlist and understand what a result actually measures. SWE-bench is relevant to repository issue resolution and test-based evaluation, but its scores depend on version and agent configuration. For terminal work, inspect terminal-focused evaluations such as Terminal-Bench; for GUI interaction, look at evaluations such as OSWorld. The existence of a result on one does not establish performance on the others.
For multilingual or multi-OS work, check whether the benchmark covers the languages and environments you use. SWE-bench-Live documents multilingual and Windows task coverage, but its changing task set and the unavailable leaderboard mean the page cannot substantiate a current ranking here. For any benchmark, ask whether the tasks are public, what the agent can access, how completion is judged and whether the configuration matches your own.
What a sensible shortlist looks like in 2026
The evidence supports testing candidates from more than one model family rather than declaring a winner from a mixed leaderboard. Databricks’ real-codebase evaluation found strong options among OpenAI, Anthropic and open-source models, and reported a high-difficulty result for GLM 5.2 in its own setup. OpenAI’s GPT-5.3-Codex announcement gives a dated provider-reported example across coding and agent benchmarks. None of these findings establishes that one of those models is best for your repository.
Start with models your chosen agent can run under the permissions and tools you intend to use. Then prioritize the shortlist based on your constraints: language and repository fit, task success, reliability, total cost, latency, environment needs and any operational requirements. Keep the evaluation tied to exact model and harness versions; results can change as either changes.
Decision
There is no defensible universal LLM winner for agentic coding in 2026. Public benchmarks and provider announcements can help identify promising candidates, while the Databricks report shows why real-codebase and harness effects matter. Pick a small shortlist, compare it under controlled conditions on representative tasks, and choose the configuration that produces the best reviewed outcomes for your team at an acceptable cost.
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