There is no single best large language model (LLM) for programming. The right choice depends on whether you are resolving repository issues, operating a terminal agent, generating code from a specification, debugging, or learning an unfamiliar codebase. Current vendor-published evaluations disagree because they measure different tasks with different harnesses and settings. Use the scores below to build a shortlist, then test those models on representative work in your own IDE or agent.
What “best for programming” actually means
Programming is a collection of different jobs. A model that edits a multi-file repository successfully may not be the fastest or clearest choice for explaining an algorithm. A terminal-agent benchmark measures whether an agent can inspect files, run commands and complete a task; it is not a general code-generation accuracy score.
- Repository engineering: fixing bugs or implementing features across an existing project.
- Terminal operation: navigating a shell, installing dependencies, running tests and recovering from failures.
- Greenfield generation: producing a function, service or script from a specification.
- Debugging and review: finding causes, proposing tests and explaining a patch.
- Learning and translation: explaining unfamiliar code or converting between languages and frameworks.
Before comparing models, write down the task, language, framework, tools the agent may use, acceptable latency and how much human review you require. Those constraints matter at least as much as a headline benchmark.
What the current published scores show
The figures in this table come from vendor pages and model cards, not an independent, controlled study. Keep the benchmark name, attempt count, harness and provider attached to every number.
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#1 Best Overall
| Model | SWE-Bench Pro | Terminal-Bench | How to read it |
|---|---|---|---|
| GPT-5.6 Sol | 64.6% (OpenAI, 2026) | 88.8% on 2.1 (OpenAI, 2026) | Strong results in both listed evaluations; provider-reported. |
| GPT-5.6 Sol Ultra | Not stated in the cited table | 91.9% on 2.1 (OpenAI, 2026) | Highest Terminal-Bench 2.1 result in OpenAI’s displayed snapshot. |
| GPT-5.6 Terra | 63.4% (OpenAI, 2026) | 87.4% on 2.1 (OpenAI, 2026) | Provider-reported results; not a universal ranking. |
| GPT-5.6 Luna | 62.7% (OpenAI, 2026) | 84.7% on 2.1 (OpenAI, 2026) | Provider-reported results; task fit still needs testing. |
| Gemini 3.5 Flash | 55.1%, single attempt (Google DeepMind, 2026) | 76.2% on 2.1 using the Terminus-2 harness (Google DeepMind, 2026) | Its model card specifies the attempt count and harness. |
| Claude Mythos 5 | 80.3% in OpenAI’s displayed comparison | Not stated there | A selected competitor entry in a provider’s table, not an exhaustive market survey. |
On these displayed values, GPT-5.6 Sol Ultra leads the listed Terminal-Bench 2.1 entries, while Claude Mythos 5 has the highest listed SWE-Bench Pro result in OpenAI’s snapshot. That does not establish either as the best model for your codebase: the evaluations target different capabilities, and the providers selected the models and conditions shown.
Why benchmark labels matter
SWE-Bench Pro focuses on repository-level software tasks. Terminal-Bench 2.1 evaluates an agent working through a terminal. A 91.9% terminal score should never be rewritten as “91.9% coding accuracy.” Likewise, a single-attempt SWE-Bench result is not directly comparable with a multi-attempt run.
Do not merge unlike evaluation runs
OpenAI reports 58.6% on SWE-Bench Pro and 82.7% on Terminal-Bench 2.0 for GPT-5.5, with reasoning effort set to xhigh in a research environment. Those benchmark versions and conditions differ from the GPT-5.6 table. Treat them as separate reports, not one time series. OpenAI also notes that API or research evaluations can differ from production ChatGPT because system prompts and available tools change.
A benchmark warning about SWE-bench Verified
In a February 2026 analysis, OpenAI said its audit of 27.6% of commonly failed SWE-bench Verified problems found at least 59.4% of the audited items had flawed tests that rejected functionally correct submissions. The analysis also described signs that some frontier models could reproduce original human fixes or problem-specific details. These are OpenAI’s findings, not a neutral benchmark-maintainer ruling; they do not prove every SWE-bench result is invalid. They do mean you should prefer newer, carefully specified evaluations such as SWE-Bench Pro when making frontier comparisons and should inspect the test setup.
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For repository bugs and features
Start with models that publish strong SWE-Bench Pro results, such as GPT-5.6 Sol (64.6% in OpenAI’s 2026 table) and the listed Claude Mythos 5 entry (80.3% in that same provider snapshot). Add the model your team already uses if it has better context handling or tooling in your IDE. Ask each candidate to modify the same issues, run the same tests and produce a reviewable diff.
For terminal-heavy agents
GPT-5.6 Sol Ultra’s 91.9% Terminal-Bench 2.1 result and GPT-5.6 Sol’s 88.8% are the highest and second-highest values in OpenAI’s displayed set. Gemini 3.5 Flash reports 76.2% with the Terminus-2 harness. These numbers indicate performance on an agentic terminal task, not that one model will be safest with production credentials or destructive commands. Restrict permissions, log commands and require confirmation for irreversible actions.
For explanations, debugging and learning
The cited evaluations do not establish a winner for explanation quality, diagnostic accuracy, language coverage or teaching. Test candidates with real failing examples from your stack. Score whether the explanation identifies the root cause, proposes a reproducer, distinguishes facts from guesses and leaves you with a focused patch.
For a particular language or framework
No cross-provider result in the available evidence settles which model is best for Python, JavaScript, Rust, Java, C#, mobile development or a specific framework. Use representative files from your own project, including build configuration and tests. A model’s general benchmark position cannot substitute for compatibility with your compiler, dependencies and repository conventions.
Run a fair comparison in your own environment
- Freeze the task. Choose three to ten issues or features that reflect normal work. Record the starting commit and acceptance tests.
- Freeze the tools. Give every model the same repository, context window, terminal permissions, system instructions and maximum number of attempts.
- Separate generation from execution. Measure whether the patch passes tests, how many commands it needed, and whether a reviewer had to repair it. Do not score prose alone.
- Record practical outcomes. Track elapsed time, failed commands, context truncation, review edits and rollback events. Keep provider-reported benchmark percentages in a separate column.
- Repeat enough to expose variance. One successful run can be luck. Run the same task more than once when your budget allows and report the attempt count.
- Review security and maintenance. Check for leaked secrets, unsafe shell commands, unpinned dependencies, missing tests and changes outside the requested scope.
A small, reproducible test harness
You can compare two already-generated patches without assuming a particular model API. Save each candidate in a clean checkout, then run the project’s test command and capture its exit status:
#!/usr/bin/env python3
import subprocess
import sys
from pathlib import Path
if len(sys.argv) != 3:
raise SystemExit("usage: score_patch.py PATCH_DIR 'TEST_COMMAND'")
patch_dir = Path(sys.argv[1]).resolve()
test_command = sys.argv[2]
result = subprocess.run(
test_command,
cwd=patch_dir,
shell=True,
text=True,
capture_output=True,
)
print(f"directory: {patch_dir}")
print(f"exit_code: {result.returncode}")
print(result.stdout)
print(result.stderr, file=sys.stderr)
raise SystemExit(result.returncode)This script does not claim that passing tests proves correctness. Pair it with human review, security checks and a record of the model, prompt, tools and attempt number.
Cost, privacy and access questions you still must verify
The evidence summarized here does not establish current cross-provider prices, quotas, latency, privacy or training-data controls, IDE integrations, regional availability or performance by programming language. Those details change frequently and can determine the practical winner. Check the plan and API terms for your region and deployment before committing source code or secrets. If private code cannot leave your network, remove hosted models from the shortlist unless your organization has an approved arrangement.
Use screenshots as part of a coding workflow
Web developers often need visual regression images, screenshots for issue reports or rendered documentation while an agent edits a site. ScreenshotNeo is a website screenshot API and MCP server for that supporting job: it accepts consent banners as a visitor and removes more than 60 known consent platforms, newsletter popups and chat widgets before capture. You can request full pages or a CSS-selected element, choose device presets or any viewport, set dark mode and retina scale, wait for a selector, delay or network idle, run custom JavaScript or CSS, hide selectors, block requests, set headers/cookies/user agent, use timezone or geolocation, return PNG/JPEG/WebP or PDF, cache with your own TTL, and submit asynchronous or bulk jobs.
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Only clean shots are billed. Bot checks or CAPTCHAs, blank pages, timeouts, failed loads and cache hits cost nothing, and each response identifies the result with X-Page-Verdict and X-Billed headers. Its MCP server exposes take_screenshot, get_page_info and capture_pdf to Claude, Cursor and other MCP clients.
Or skip the browser setup
One GET request returns the rendered asset. The examples below use the documented API; see the ScreenshotNeo documentation for authentication and options.
curl -G "https://api.screenshotneo.com/v1/shot" -d access_key=YOUR_API_KEY --data-urlencode url=https://stripe.com -o shot.webpimport requests
r = requests.get("https://api.screenshotneo.com/v1/shot", params={"access_key": "YOUR_API_KEY", "url": "https://stripe.com"}, timeout=90)
open("shot.webp", "wb").write(r.content)const q = new URLSearchParams({ access_key: 'YOUR_API_KEY', url: 'https://stripe.com' }); const res = await fetch(`https://api.screenshotneo.com/v1/shot?${q}`);There is a free allowance of 1,000 screenshots per month with no card. Paid plans start at $5 for 3,000 shots; every feature is included on every plan. Create a free ScreenshotNeo account to try it.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Troubleshooting a model evaluation
The model edits the wrong files
Provide the repository root, explicit scope and a required diff review. Restrict write permissions to the task directory and ask for a plan before edits.
Tests pass locally but fail in CI
Capture the exact runtime, dependency lockfile, environment variables and test command. Re-run in the same container or runner for every candidate.
Best Value
The agent loops in the terminal
Set a command and time budget, stop after repeated identical failures and require the agent to summarize the blocker. A high Terminal-Bench score does not remove the need for guardrails.
Scores disagree across pages
Check benchmark version, harness, attempt count, effort setting, selected models and whether the result is provider-reported. Do not average incompatible percentages.
A screenshot used for a visual test is cluttered
Use ScreenshotNeo’s cookie-consent handling, popup and chat-widget removal, selector hiding and wait controls. Inspect X-Page-Verdict and X-Billed to distinguish a clean capture from a failed load.
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Choose the LLM that wins on your representative tasks under the same tools and review standard. Current vendor evidence suggests GPT-5.6 Sol and Sol Ultra are strong candidates for repository and terminal work, while the displayed SWE-Bench Pro table also includes a higher Claude Mythos 5 entry; none of that proves a universal winner. Treat benchmark labels and provider attribution as part of the result, verify privacy and cost for your deployment, and keep a human responsible for the final code.
Frequently Asked Questions
Are Terminal-Bench and SWE-Bench scores interchangeable?
No. Terminal-Bench measures agentic command-line work, while SWE-Bench targets repository-level software tasks; their scores should not be compared as one accuracy scale.
Does a higher benchmark score guarantee better code in my IDE?
No. IDE tools, context, language, repository conventions, latency, permissions and review requirements can change the outcome.
Should I use SWE-bench Verified to pick a model?
Use caution. OpenAI’s February 2026 audit reported substantial test flaws in an audited subset, so inspect benchmark design and favor well-specified comparisons such as SWE-Bench Pro.
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Can an AI agent take browser screenshots without writing Playwright code?
Yes. ScreenshotNeo provides an MCP server and a GET API, with controls for consent banners, popups, waits, selectors, devices and output formats.
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