Which AI model is best for coding in 2026? We rank major LLMs by SWE-bench Pro and LiveCodeBench, with SWE-bench Verified shown as a historical baseline and React Native Evals tracked as a display benchmark for mobile app work.
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The coding leaderboard changed after BenchLM started weighting SWE-Rebench properly. GPT-5.4 now leads the current coding table at 73.9, followed by Claude Opus 4.6 at 72.5 and Kimi K2.5 (Reasoning) at 70.4.
BenchLM.ai's current coding score weights SWE-Rebench, SWE-bench Pro, LiveCodeBench, and SWE-bench Verified. HumanEval is still useful as context, but it is too saturated to drive the main coding rank by itself.
One newer display benchmark worth watching is React Native Evals. It does not affect BenchLM's weighted coding rank today, but it fills a real coverage gap by testing framework-specific mobile app implementation work that generic repository and competitive-programming benchmarks do not capture well. If React Native or Expo-style product work matters in your stack, read the React Native Evals explainer alongside the main coding leaderboard.
| Model | SWE-Rebench | SWE-bench Pro | LiveCodeBench | SWE-bench Verified | Coding score |
|---|---|---|---|---|---|
| GPT-5.4 | — | 57.7 | 84 | 84 | 73.9 |
| Claude Opus 4.6 | 65.3 | 74 | 76 | 80.8 | 72.5 |
| Kimi K2.5 (Reasoning) | 57.4 | 70 | 85 | 76.8 | 70.4 |
| GPT-5.2 | — | 55.6 | 79 | 80 | 70.2 |
| GLM-4.7 | — | 51 | 84.9 | 73.8 | 69.3 |
| Gemini 3.1 Pro | 62.3 | 72 | 71 | 75 | 68.8 |
| GPT-5.3 Codex | 58.2 | 56.8 | 85 | 85 | 68.6 |
| MiMo-V2-Flash | — | 52 | 80.6 | 73.4 | 67.9 |
| Grok 4 | — | 48 | 79.4 | 73 | 65.8 |
| MiniMax M2.7 | — | 56.22 | — | 78 | 64.4 |
| Claude Sonnet 4.6 | 60.7 | 64 | 54 | 79.6 | 62.7 |
| GLM-5 (Reasoning) | — | 67 | 58 | 62 | 62.4 |
Scores from BenchLM.ai leaderboard. Prices per million tokens.
GPT-5.4 now leads the coding leaderboard because it is strong across every benchmark that still matters. Claude Opus 4.6 stays close because it combines a strong SWE-Rebench row with solid SWE-bench Pro and LiveCodeBench scores.
The gap between GPT-5.3 Codex and GPT-5.4 Pro on SWE-bench Verified is one point (85 vs 86). On LiveCodeBench it's also one point (85 vs 86). For nearly every practical coding task, the performance difference will be imperceptible. The cost difference is not.
For an AI coding assistant generating 10M output tokens per month, GPT-5.3 Codex costs $100/month. GPT-5.4 Pro costs $1,800/month. That math drives model selection at any meaningful scale.
OpenAI's coding model lineup in 2026 is confusing. Here's how to read it:
"Codex" suffix = coding-specialized variant. Higher SWE-bench scores but may underperform general models on open-ended chat and reasoning.
"Spark" suffix = lighter, faster variant. GPT-5.3-Codex-Spark ($2/$8) scores 85 SWE-bench Pro vs GPT-5.3 Codex's 90, but costs 20% less on input and 20% less on output.
"Pro" suffix = highest-capability flagship. GPT-5.4 Pro and GPT-5.2 Pro lead on overall benchmarks but are priced for enterprise budgets.
The practical tiers for coding:
Short completions (under 50 tokens) don't require SWE-bench-level capability. The latency and cost profile matter more than marginal benchmark differences.
Best options: GPT-5.3-Codex-Spark ($2/$8) for quality completions, Gemini 3.1 Pro ($1.25/$5) for cost-sensitive high-volume use. Both score 91%+ on HumanEval.
This is exactly what SWE-bench measures. GPT-5.3 Codex (90) and GPT-5.4 (85) are the clear choices. Claude Opus 4.6 scores 74 on SWE-bench Pro — notable for being the best non-OpenAI option, but 16 points behind the leader.
Best option: GPT-5.4 or Claude Opus 4.6 if you want the strongest fully-ranked frontier coding rows. GPT-5.3 Codex still looks strong on raw benchmark lines, but its coding row is less dominant now that SWE-Rebench is weighted.
Agentic coding burns tokens fast. Terminal-Bench 2.0 measures performance in terminal-based coding environments — OpenAI models score 90 across the board, Claude Opus 4.6 scores 80.
The cost factor is critical for agents: Claude Opus 4.6 at $15/$75 adds up quickly in agent loops. GPT-5.3 Codex at $2.50/$10 is the far more sustainable choice for agents making hundreds of calls.
Best option: GPT-5.4 for top-end quality, GPT-5.2 or GPT-5.3 Codex for value, and Claude Sonnet 4.6 for teams that want Anthropic's tooling stack.
LiveCodeBench pulls fresh competitive programming problems continuously — GPT-5.4 Pro leads at 86, GPT-5.3 Codex at 85. DeepSeek Coder 2.0 at 45 is a significant drop-off.
Best option: GPT-5.4 Pro if budget allows, GPT-5.3 Codex for value.
No dedicated SQL benchmark exists at frontier level yet. Based on structured output and reasoning scores, GPT-5.4 and GPT-5.3 Codex both handle complex SQL reliably. For batch data pipelines, Gemini 3.1 Pro ($1.25/$5) is the cost-effective choice.
Test generation is underrepresented in benchmarks. Strong SWE-bench performance correlates with good test generation since fixing bugs often requires writing regression tests. GPT-5.3 Codex and GPT-5.4 are both reliable here.
The open-source coding landscape in 2026 is weaker than the frontier for hard software engineering tasks:
For teams that need fully self-hosted models, the quality ceiling for open-source coding in 2026 is considerably below the frontier. The 25-30 point SWE-bench gap between DeepSeek Coder 2.0 and GPT-5.3 Codex is large enough to matter in production.
Need the best possible coding model: GPT-5.4 or Claude Opus 4.6. GPT-5.4 currently leads, but the gap is not huge.
Running an AI coding agent at scale: GPT-5.3 Codex. The agent loop cost math makes $30/$180 unsustainable for most teams.
Claude ecosystem: Claude Opus 4.6 (74 SWE-bench Pro) or Claude Sonnet 4.6 (64 SWE-bench Pro). Both significantly behind GPT-5.3 Codex on coding benchmarks. Worth the tradeoff only if other Claude capabilities matter more for your workflow.
Budget-first coding: MiniMax M2.7, DeepSeek Coder 2.0, or MiMo-V2-Flash depending on whether you care more about API price, open weights, or LiveCodeBench-style coding.
→ See the full coding leaderboard · Compare SWE-bench scores · LiveCodeBench details · React Native Evals explainer
What is the best LLM for coding in 2026? Right now it is GPT-5.4 on BenchLM's coding leaderboard, followed by Claude Opus 4.6 and Kimi K2.5 (Reasoning).
How does Claude compare to GPT for coding? Claude Opus 4.6 is now much closer to the top GPT rows than older snapshots suggested. GPT-5.4 still leads, but Claude Opus 4.6 is now the #2 coding row on BenchLM.
Is SWE-bench a good benchmark for coding AI? Yes — it's the most reliable coding signal available. It tests real bug-fixing on actual GitHub repositories, not toy functions. HumanEval is saturated (multiple models at 95%) and no longer differentiates frontier models.
What's the best coding model for an AI agent? GPT-5.4 if you want the strongest frontier blend, Claude Opus 4.6 if you prefer Anthropic, and GPT-5.2 / MiniMax M2.7 if cost sensitivity matters more.
Should I use GPT-5.4 Pro or GPT-5.3 Codex for coding? Not automatically. GPT-5.4 Pro still has elite raw coding numbers, but it is now treated as a sparse row on BenchLM's category leaderboard. GPT-5.3 Codex is still strong, but less dominant than before once SWE-Rebench is included.
Benchmark scores from BenchLM.ai. Prices per million tokens, current as of March 2026.
Coding benchmarks shift with every model release. We send one email a week with what moved and why.
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