Top 7 LLMs for Coding in 2026 (SWE-bench, LiveCodeBench, Terminal-Bench)
We ranked the seven strongest coding models of early 2026 primarily by SWE-bench Verified, the most contamination-resistant agentic coding benchmark, with LiveCodeBench and Terminal-Bench 2.0 as secondary axes. Every score below is labeled as vendor-reported or independently measured, and the numbers reflect February to March 2026 snapshots that will shift over time.
The Full Rankings
This table covers all seven models at a glance. Click any model name to jump to its detailed breakdown below, or sort by any column to re-rank on a different axis. The Source column flags whether the figures are independently measured or include vendor-reported numbers. Scores are SWE-bench Verified percent, LiveCodeBench percent, and Terminal-Bench 2.0 percent.
| # ↕ | Model ↕ | Org ↕ | SWE-bench Verified ↕ | LiveCodeBench ↕ | Terminal-Bench 2.0 ↕ | Source ↕ |
|---|---|---|---|---|---|---|
| 1 | Claude Opus 4.6 | Anthropic | 80.8SWE lead | 76.0 | 65.4 | Vendor + Independent |
| 2 | Gemini 3.1 Pro | 80.6 | 81.3 | 68.5 | Independent | |
| 3 | MiniMax M2.5 | MiniMax | 80.2 | 65.0 | 42.2 | Vendor + Independent |
| 4 | GLM-5 | Zhipu AI | 77.8 | 52.0 | 56.2 | Independent |
| 5 | Kimi K2.5 | Moonshot AI | 76.8 | 85.0 | Not reported | Independent |
| 6 | Qwen 3.5-plus | Alibaba | 76.4 | 83.6LCB lead | 52.5 | Independent |
| 7 | GPT-5.3 Codex | OpenAI | Not independently reported (SWE-bench Pro 56.8) | 71.0 | 77.3Term lead | Independent |
Sortable: click "SWE-bench Verified", "LiveCodeBench", or "Terminal-Bench 2.0" to re-rank. The two rows without an independent SWE-bench Verified figure sort to the bottom of that column.
How We Ranked These Models
Ranked primarily by SWE-bench Verified (real GitHub issues, contamination-resistant), with LiveCodeBench and Terminal-Bench 2.0 as secondary axes, as of February to March 2026. SWE-bench scores depend on the agentic scaffold. Vendor-reported versus independent is labeled. Saturated benchmarks such as HumanEval and GSM8K are excluded.
Where a model lacks an independently reported SWE-bench Verified score, we say so rather than substitute a different benchmark and treat it as equivalent. GPT-5.3 Codex is placed at the bottom of the primary axis for this reason and is included on the strength of its independent Terminal-Bench 2.0 result.
The three benchmarks measure different things, and a model can lead one while trailing another:
- SWE-bench Verified runs a model inside an agent harness against human-validated GitHub issues. It is the closest public proxy for "can this fix a real bug in a real repository," which is why it is our primary axis.
- LiveCodeBench uses a rolling set of recent competitive-programming problems collected after model training cutoffs, making it among the most contamination-resistant trackers for algorithmic coding.
- Terminal-Bench 2.0 measures agentic shell and tool-use competence: navigating a filesystem, running commands, and completing multi-step terminal tasks.
Sources include independent trackers (Onyx AI, LLM-Stats, Artificial Analysis) and the public SWE-bench, LiveCodeBench, and Terminal-Bench leaderboards, cross-checked against vendor technical reports where independent figures were unavailable. Saturated benchmarks like HumanEval (above 93 percent across the field) and GSM8K (around 99 percent) are excluded because they no longer separate frontier models.
1. Claude Opus 4.6 (Anthropic)
Claude Opus 4.6 takes the top spot on our primary axis with the highest SWE-bench Verified score in the group at 80.8 percent. The gap over second place is small, well within scaffold-driven noise, but Opus has been the most consistent performer across independent harnesses on real repository fixes. Its Terminal-Bench 2.0 result of 65.4 is mid-pack, and its LiveCodeBench figure of 76.0 trails the algorithmic specialists, so it is a strong generalist rather than a category winner everywhere.
Read more: What Is Claude AI?
2. Gemini 3.1 Pro (Google)
Gemini 3.1 Pro is arguably the strongest all-rounder on this list. Its independent SWE-bench Verified result of 80.6 is statistically level with the leader, and it pairs that with a much higher LiveCodeBench score of 81.3 and the second-best Terminal-Bench 2.0 figure at 68.5. If you want one model that is competitive on real bug-fixing, algorithmic problems, and terminal agents at once, Gemini 3.1 Pro has the most balanced profile here.
Read more: What Is Google Gemini?
3. MiniMax M2.5 (MiniMax)
MiniMax M2.5 rounds out the top tier on SWE-bench Verified at 80.2 percent, a genuinely strong result for real-repository bug fixing. The catch is that its strength is narrow: its LiveCodeBench score of 65.0 and especially its Terminal-Bench 2.0 result of 42.2 are the weakest agentic-terminal figure in the group. It is a focused repository-fixing model more than a do-everything coding assistant.
4. GLM-5 (Zhipu AI)
GLM-5 posts a solid independent SWE-bench Verified score of 77.8, landing just below the top tier. Its Terminal-Bench 2.0 result of 56.2 is respectable, sitting between the leaders and the weaker terminal performers. The soft spot is LiveCodeBench at 52.0, the lowest algorithmic score here, which suggests it is more comfortable with applied repository work than with competitive-style problems.
5. Kimi K2.5 (Moonshot AI)
Kimi K2.5 is a study in why the primary axis matters. Its independent LiveCodeBench score of 85.0 is the highest algorithmic figure on this entire list, yet its SWE-bench Verified result of 76.8 places it fifth on our real-repository axis. No Terminal-Bench 2.0 figure has been independently reported for it. If competitive-style problem solving is your priority, Kimi punches well above its overall rank.
6. Qwen 3.5-plus (Alibaba)
Qwen 3.5-plus holds the LiveCodeBench crown among models with a full score set at 83.6 percent, edging out Gemini on that axis. Its SWE-bench Verified result of 76.4 is solid but places it sixth on our primary measure, and its Terminal-Bench 2.0 figure of 52.5 sits mid-table. Like Kimi, it is a reminder that a high algorithmic score does not automatically translate into top real-repository performance.
7. GPT-5.3 Codex (OpenAI)
GPT-5.3 Codex is the hardest model to place, which is exactly why it ranks seventh on a SWE-bench-first list. It has no independently reported SWE-bench Verified score; the closest public figure is SWE-bench Pro at 56.8, a harder and different benchmark that cannot be compared directly to the Verified numbers above. Where it clearly excels is agentic terminal work: it leads the group on Terminal-Bench 2.0 at 77.3, with a respectable LiveCodeBench result of 71.0.
Read more: What Is ChatGPT?
Reading the Scores: Three Things That Change the Ranking
Benchmark tables look authoritative, but coding scores carry caveats that can flip a ranking. Keep these three in mind before you treat any number as settled.
SWE-bench Verified and Terminal-Bench 2.0 run a model inside an agent harness. The same model can score noticeably higher or lower depending on the scaffold, for example Claude Code versus a generic Codex-style CLI. Read every figure as a model-plus-scaffold result, not pure model capability.
HumanEval (above 93 percent across the field) and GSM8K (around 99 percent) are saturated and contamination-prone, so they no longer separate frontier models. We exclude them from ranking. If a comparison leans on those numbers, treat its conclusions with caution.
Newer releases such as DeepSeek V4-Pro and Kimi K2.6 lack public, independently reported coding benchmarks at the time of writing. We do not rank what we cannot verify. The table will be revised when contamination-resistant scores are published, rather than filled in with estimates.
The practical takeaway: the top three on SWE-bench Verified are effectively tied, so pick based on your actual workload. Choose for real-repository bug fixing on the SWE-bench axis, for algorithmic problems on LiveCodeBench, and for terminal-agent tasks on Terminal-Bench 2.0. Then verify current standings yourself, because these are February to March 2026 snapshots and the leaderboards move.
Frequently Asked Questions
What is the best LLM for coding in 2026?
By SWE-bench Verified, the most contamination-resistant agentic coding benchmark, Claude Opus 4.6 leads at 80.8 percent, with Gemini 3.1 Pro at 80.6 and MiniMax M2.5 at 80.2 close behind. That top three is effectively tied. The right model depends on your task: Qwen 3.5-plus leads LiveCodeBench (83.6) among full-profile models for competitive-style problems, and GPT-5.3 Codex leads Terminal-Bench 2.0 (77.3) for shell and tool-use work.
Why is SWE-bench Verified used as the primary ranking signal?
SWE-bench Verified draws from real, human-validated GitHub issues, so it measures whether a model can resolve genuine software bugs rather than recite memorized answers. It is more contamination-resistant than saturated benchmarks like HumanEval (above 93 percent) and GSM8K (around 99 percent), which we exclude. The main caveat is that SWE-bench scores depend on the agentic scaffold used to run the model.
What does scaffold-dependent mean for these scores?
Agentic coding benchmarks run a model inside a harness that plans, edits files, runs tests, and retries. The same model can score noticeably higher or lower depending on the scaffold, for example Claude Code versus a generic Codex-style CLI. Compare scores as model-plus-scaffold results, not pure model capability, and treat small gaps between top models as noise.
Why is GPT-5.3 Codex ranked seventh if it leads Terminal-Bench?
Our primary ranking axis is SWE-bench Verified, and GPT-5.3 Codex has no independently reported Verified score at the time of writing. Its available figure is SWE-bench Pro at 56.8, a harder and different benchmark that cannot be placed on the same axis as the others. It still leads Terminal-Bench 2.0 at 77.3, which is why it earns a spot on the list.
Where do DeepSeek and Kimi fit in this ranking?
Kimi K2.5 is included at rank five, and it actually leads LiveCodeBench at 85.0. Newer releases such as DeepSeek V4-Pro and Kimi K2.6 lack public, independently reported coding benchmarks at the time of writing, so we do not rank them. We will revise the table once contamination-resistant scores are published rather than estimate placements.
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