Best LLM for Coding (2026): 12 Models Ranked by SWE-bench and Cost per Task

Best LLM for Coding: Quick Answer (July 2026)

The best LLM for coding in July 2026 is Claude Fable 5 (95.0% SWE-bench Verified), back on the API since July 1. For most teams that do not need the ceiling, Claude Opus 4.8 (88.6%, $5/$25) is the practical pick, and GPT-5.5 ties it at 88.7%. The best open-weight LLM is DeepSeek-V4-Pro-Max (80.6%), self-hostable under MIT. Cheapest per solved task: Claude Haiku 4.5, about $0.13 of output per benchmark point.

"Best LLM for coding" and "best AI model for coding" are the same question, and this page answers both. It divides the two columns every other ranking keeps apart, and adds a third that almost no one measures: SWE-bench Pro scores from Scale's standardized leaderboard, official per-token prices, and the output-dollar cost per solved benchmark point. Updated July 13, 2026: Claude Fable 5 restored (July 1), scores and prices refreshed, and the open-weight models you can run on Morph (GLM-5.2, Qwen 3.5, DeepSeek V4 Flash, MiniMax M3) added with real numbers.

12 Models Ranked: SWE-bench Pro x Price x Cost per Solved Point

On a standardized harness, gpt-5.4 solves the most SWE-bench Pro tasks (59.10%) and Claude Haiku 4.5 solves them for the least money (about $0.13 of output per point). The table below uses Scale's SEAL public-set leaderboard, which runs every model through the same scaffolding on SWE-bench Pro (1,865 tasks, 41 professional repositories, scored Pass@1). The last column divides official output price by score: dollars of output tokens per benchmark point solved. Lower is more cost-effective.

Three things fall out of the combined view. GPT-5.4 wins on score and is competitive on cost per point at $0.25. Haiku 4.5 solves 67% as many tasks as gpt-5.4 at a third of its output price, making it the cost-per-point leader at roughly $0.13. And Opus 4.6, the top Claude entry Scale has tested, pays a 2x cost-per-point premium over gpt-5.4 for 7.2 fewer points on this harness, which is exactly why Anthropic publishes its own numbers (covered below).

Cost per Completed Task, Not per Token

The per-token price on a model card is close to useless for budgeting a coding agent. What you pay is the price of every token the model burns to finish a task: reasoning tokens, retries, and tool-call round trips. A model that is 12x cheaper per token can end up more expensive per task if it thinks 20x longer, and a model that is pricier per token can be cheaper per task if it solves in one pass. Rank by cost per completed task on your own traffic, not by the headline rate.

Artificial Analysis now scores coding agents on exactly this: average pay-per-token API cost per task, counting input, cache, reasoning, and output tokens separately (Coding Agent Index methodology). The gap this exposes is large. In one worked comparison, Cursor Composer costs $2.50/M output against GPT-5.5's $30/M, a 12x per-token difference, but averages about $0.07 per coding task versus $4.82, a 69x per-task difference, because the workhorse model burns far fewer tokens to close each task (UsageBox, 2026).

Two rules fall out of this for picking a coding LLM. First, a reasoning-heavy frontier model with a low per-token headline can still be the most expensive line on your bill, so measure tokens-per-task before you commit. Second, the cheapest per-token open models (DeepSeek V4 Flash at $0.14/$0.28, MiniMax M3 at $0.60/$2.40) only win on cost per task if they also solve in comparable token counts, which is why a difficulty router that sends the easy 80% to a cheap model and reserves a frontier model for the hard 20% beats any single-model default. The routing section below has the split.

SWE-bench Verified Leaderboard (July 2026)

On SWE-bench Verified, Claude Fable 5 leads at 95.0% and is buyable again as of July 1; the next tier is GPT-5.5 (88.7%) and Claude Opus 4.8 (88.6%), a near tie. SWE-bench Verified is older, Python-only, and partially contaminated, but it is still the number every launch post quotes. Every score below is vendor self-reported; the llm-stats tracker lists 0 of them as independently verified. The top ten:

Two structural facts in this chart. First, the Claude line pulled away at the very top: Fable 5's 95.0% is 6.4 points above Opus 4.8 and 14.4 above the 80-percent cluster. With Fable 5 restored on July 1 it is the live ceiling again, though Opus 4.8 at $5/$25 is half its price. Second, the 80-percent cluster is now mostly open weights. DeepSeek-V4-Pro-Max (80.6%) ties Gemini 3.1 Pro exactly, and you can download its MIT-licensed weights.

Which Claude Model Is Best for Coding?

Claude Fable 5 (claude-fable-5) is the highest-scoring Claude for coding at 95.0% SWE-bench Verified and is buyable again as of July 1, but at $10/$50 it is twice the price of the everyday default, Claude Opus 4.8 (claude-opus-4-8): 88.6% SWE-bench Verified, 69.2% SWE-bench Pro on Anthropic's harness, $5/$25 per million tokens, 1M context with no long-context surcharge. Anthropic ships five current coding-relevant models. Exact API IDs, prices, and the decision logic:

One model to set aside: Claude Mythos 5 (93.9% SWE-bench Verified as Mythos Preview) is a limited-availability model restricted to approved Project Glasswing partners. Unlike Fable 5, which returned to the API on July 1, Mythos 5 remains limited to approved partners. There is no self-serve access, so it is not a practical coding pick.

Claude Opus 4.8 vs GPT-5.5: The $5-Input Flagships

Opus 4.8 and GPT-5.5 both cost $5/M input and tie on SWE-bench Verified (88.6% vs 88.7%); Opus 4.8 wins repo-scale software engineering on SWE-bench Pro (69.2% vs 58.6%) and costs less output ($25 vs $30), while GPT-5.5 is the model OpenAI now ships through Codex. The splits:

Opus 4.8 wins repo-scale software engineering (69.2% vs 58.6% SWE-bench Pro on vendor harnesses) and is cheaper on output ($25 vs $30 per M). GPT-5.5 edges SWE-bench Verified by 0.1 point and is what OpenAI now points Codex at: the dedicated -codex variants are retired, so the general gpt-5.5 is the current Codex model. If your work lives in a CLI agent, Codex pricing changes the math; if it lives in long-horizon repo edits, Opus 4.8 does.

Open-Source Models: 80% SWE-bench Verified at a Tenth of the Price

The best open-source coding model in July 2026 is DeepSeek-V4-Pro-Max at 80.6% SWE-bench Verified, tied with Gemini 3.1 Pro and self-hostable under MIT. MiniMax M3 matches it at 80.5% with a 1M context window, and Kimi K2.6 reaches 80.2%. Official API prices and self-host terms:

The arithmetic that matters: MiniMax M3 produces output at $2.40/M against Opus 4.8's $25/M, a 10.4x gap, while trailing it by 8.1 points on SWE-bench Verified (80.5 vs 88.6). DeepSeek V4 Flash sets the absolute floor at $0.28/M output with a 1M-token context. The price gap is mostly model size, not provider margin, which is the core fact of how AI inference is priced. For teams with data-sovereignty requirements, DeepSeek V4's MIT license means the 80.6% model is self-hostable outright. Deeper coverage on the best open-source coding model page.

What People Actually Code With (r/LocalLLaMA, mid-2026)

Benchmark tables and the models people run every day have drifted apart. On r/LocalLLaMA, the open-weight coding conversation in mid-2026 is dominated by GLM-5.2 and Qwen 3.6, not by whichever model tops SWE-bench that week. The recurring theme: a model you can actually run, at a token budget you can afford, beats two extra benchmark points you pay frontier prices for.

The most-upvoted open-weight coding threads on the subreddit this cycle are about running GLM-5.2 locally, not about the leaderboard. "GLM-5.2 is a win for local AI" (1,200+ upvotes, 315 comments) and "GLM-5.2 on 5x Pro 6000s and a 5090, an expensive journey" (1,500+ upvotes) are the community reckoning with what it costs to self-host a 744B MoE well enough to code against. The Qwen side shows up as "Qwen3.6 35B-A3B (Q8_0, no KV quant) single prompt", a 35B active-3B model people run unquantized on a single box.

Two practitioner signals matter for a "best LLM for coding" pick. The GPU math is brutal: threads describe five RTX Pro 6000s plus a 5090 to serve GLM-5.2 at usable speed, which is why most teams that want the open model's output rent it hosted rather than build the rack. And the export-control drama around Claude Fable and gated GPT-5.6 ("US Govt to individually approve who gets GPT 5.6") is a real availability risk that pushed developers toward open weights they control. Running GLM-5.2 or DeepSeek V4 on a hosted OpenAI-compatible API like Morph is the middle path: the open model's output without the rack.

Best AI Model for Coding at $0

The best free path to real coding capability in July 2026 is DeepSeek V4's MIT-licensed open weights (self-host) or its near-free hosted API at $0.14/$0.28. Four no-credit-card options:

The honest framing: free tiers are for evaluation and light use. DeepSeek V4 Flash's paid API at $0.14/M input ($0.0036/M on cache hits) and $0.28/M output is close enough to zero that most teams skip self-hosting unless data cannot leave their network.

Why Vendor Scores Run 20 Points Above Scale's Leaderboard

Anthropic reports Fable 5 at 80.0% on SWE-bench Pro. Scale's standardized leaderboard tops out at 59.10% (gpt-5.4). Both numbers are real. The difference is the harness: Scale runs every model through identical scaffolding; vendors run their own tuned agent stacks. (Vendor SWE-bench Verified numbers are also self-reported; llm-stats lists 0 as independently verified.)

The practical conclusion has not changed since 2025: the scaffold around the model accounts for more variance than swapping frontier models. Before paying a 2x token premium, fix retrieval, context management, and tool design. Subagent architecture and context engineering move scores more than model choice does.

Per-Task Routing: Which Model for Which Job

The most cost-effective setups in July 2026 route by task, not by loyalty: send the hard 20% to Opus 4.8 and the cheap 80% to Haiku 4.5 or DeepSeek V4 Flash. Numbers-backed defaults:

Cost levers that apply across routes: Anthropic's Batch API is 50% off input and output, prompt-cache reads are 0.1x base input, and DeepSeek cache hits drop input to $0.0036/M. A routing setup that pins 80% of traffic to Haiku 4.5 or DeepSeek V4 Flash and reserves Opus 4.8 for the hard 20% typically beats any single-model subscription. Doing the split automatically needs a classifier: Morph's Router scores each prompt by difficulty and domain in ~180ms and returns the cheapest capable model, so a coding agent gets cheaper and faster at once (see the model lineup and pricing). Claude Code Router makes that per-request routing concrete inside the terminal agent, and Claude Code models covers harness-side defaults.

Frequently Asked Questions

Sources

Primary sources behind the scores and prices on this page (updated July 13, 2026):

• Scale SEAL SWE-bench Pro public leaderboard (standardized harness scores)
• llm-stats SWE-bench Verified tracker and SWE-bench Pro aggregate (vendor self-reported)
• SWE-bench Pro paper (1,865 tasks / 41 repos, benchmark definition)
• Anthropic Claude pricing and Fable / Mythos access notice (suspension)
• OpenAI API pricing, Codex models, and GPT-5.6 preview
• Google Gemini API pricing
• DeepSeek API pricing and Z.ai GLM-5.2 docs
• Meta Muse Spark announcement
• Artificial Analysis Coding Agent Index methodology (cost per task, token usage per task)
• UsageBox: cost per task vs per-token pricing (Composer vs GPT-5.5 per-task figures)
• r/LocalLLaMA (practitioner threads on GLM-5.2 and Qwen 3.6 for local coding, mid-2026)