Best LLM for Coding in 2026: Benchmark Data, Cost per Solved Task & Picks

“Best LLM for coding” is mostly answered with vibes: whichever model a vendor is promoting that month. This page answers it with data instead. We take the two benchmarks that best predict real coding-agent performance - SWE-bench Verified (resolving real GitHub issues) and Terminal Bench (multi-step work in a live terminal) - combine them with API pricing, and compute what actually matters for your bill: cost per solved task, not cost per token.

All numbers come from the RankLLMs dataset (last verified September 2026, sources and weights in our methodology). The live version of this table is on the AI leaderboard.

The Data: Top Coding Models Compared

*Cost per solved task assumes a representative agentic coding run of ~20K input + ~5K output tokens per attempt (a mid-size repo task with context and retries), divided by benchmark accuracy. Your task sizes will differ; the ratios are what matter.

Three things in this table should change how you shop for a coding model.

Finding 1: The Quality Ceiling Is Crowded - and Expensive to Join

Claude Fable 5 tops the board at 84.5 SWE-bench Verified, but Claude Opus 5, GPT-5.6 Sol, DeepSeek-V4-Pro, and Kimi K3 are all within 4 points. The gap between the #1 and #5 coding model is smaller than the gap between any two consecutive pricing tiers. Paying Fable 5’s $14.44 blended rate buys you roughly 3.5 points of accuracy over DeepSeek-V4-Pro - which is real, and worth it for the hardest tasks, but it is not a 9-times-better model even though it is a 9-times-more-expensive one per solved task ($0.43 vs $0.06).

The pattern that actually works in production: a mid-tier workhorse for 90% of tasks, with the quality leader reserved for the hardest 10% via routing. Most agent frameworks now support this natively.

Finding 2: The Value Champions Are Not Who You Expect

Cost per solved task reorders the market dramatically:

• Gemini 3.7 Flash is the value king of the flagship tier. At $0.04 per solved task with 78.6 SWE-bench and an 85.8 Terminal Bench, it delivers about 93% of the best model’s coding quality for under 10% of the cost. Terminal Bench 85.8 - nearly tied with Fable 5’s 88.0 - means it holds up in live terminal work, not just static benchmarks.
• DeepSeek-V4-Pro-0813 is the open-weights workhorse. 81.0 SWE-bench Verified at $0.06 per solved task, with open weights for self-hosting. It is within 1 point of GPT-5.6 Sol at less than a third of the cost.
• GLM-5.3-Flash is the ultra-budget surprise. $0.19 per million tokens - roughly 40x cheaper than Fable 5 - while scoring 78.2 SWE-bench and 84.3 Terminal Bench. At $0.006 per solved task, you can run 70 attempts for the price of one Fable 5 attempt. For high-volume, lower-stakes automation (batch refactors, test generation, code review triage), the math is not close.

Finding 3: Terminal Bench Predicts Agent Experience Better Than SWE-bench

Two models illustrate why we track both benchmarks. Grok 4.6 scores a strong 78.5 SWE-bench Verified but a weak 26.0 Terminal Bench - it resolves issues on paper yet struggles in live terminal environments, which is exactly what CLI coding agents do all day. Meanwhile GLM-5.3 pairs the table’s second-best Terminal Bench (88.3) with a mid-pack SWE-bench score.

If you are choosing a model to power a terminal agent (Claude Code, Muse Code, OpenCode and their equivalents), weight Terminal Bench heavily. If you are building issue-resolution pipelines against structured repos, SWE-bench Verified is the better predictor. Our SWE-bench Pro guide covers why version and harness details matter for both.

The Picks

• Best overall coding model: Claude Fable 5 - the accuracy and Terminal Bench leader. Worth it for hard engineering, architecture-level refactors, and when a failed attempt costs more than the tokens.
• Best flagship value: Gemini 3.7 Flash - near-frontier quality, the lowest cost per solved task among big-name models.
• Best open-weights / self-hosted: DeepSeek-V4-Pro-0813 - frontier-adjacent coding at open-weights pricing, with the license freedom Apache-style hosting brings.
• Best ultra-budget: GLM-5.3-Flash - 78% SWE-bench accuracy at a price that makes volume automation viable.
• Best for terminal agents: Claude Fable 5 or GLM-5.3, depending on budget - both clear 88 Terminal Bench.

For head-to-head details on the agents themselves (not the models), see Muse Code vs Claude Code, and for how 2024’s models set the stage, see our Claude 3.5 Sonnet benchmark history.

How We Choose and When This Updates

Picks are driven by the dataset: benchmark accuracy first, then cost per solved task, then Terminal Bench for agent workloads. We do not accept placement deals - that rule is in our editorial policy. This page is re-verified monthly and whenever a major release lands; model-by-model sources and dates are on each model scorecard.

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• AI Models Directory
• LLM News & AI Model Updates
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