Best AI Model for Coding (2026): 13 Models Ranked by Benchmarks and Cost per Task

Best AI Model for Coding: Quick Answer (July 2026)

The best AI model for coding in July 2026 depends on the job. Top raw capability: GPT-5.6 Sol (96.2% SWE-bench Verified, Vals AI independent), then Claude Fable 5 (95.0%) and Kimi K3 (93.4%). Best for frontend and UI: Kimi K3, the first open model to top the Arena.ai Frontend Code Arena, ahead of Fable 5. Everyday default: Claude Opus 4.8 (88.6%, $5/$25). Best open-weight value: GLM-5.2 ($1.10/$4.10 on Morph), top open model on the Artificial Analysis Intelligence Index.

"Best AI model for coding" and "best LLM for coding" are the same question, and this page answers it by cost per completed task. It divides the two columns every other ranking keeps apart, and adds the ones almost no one measures: an independent SWE-bench Verified harness (Vals AI), the Arena.ai Frontend Code Arena, Scale's standardized SWE-bench Pro leaderboard, official per-token prices, and the output-dollar cost per solved benchmark point. For the same models ranked picks-by-job rather than by cost, see best LLM for coding. Updated July 24, 2026: GPT-5.6 Sol reached general availability July 9 and leads Vals AI at 96.2%, Kimi K3 took #1 on the Frontend Code Arena, GLM-5.2 became the top open model on the Artificial Analysis Intelligence Index, and DeepSeek V4 went GA on July 19.

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

On a standardized harness, Muse Spark 1.1 solves the most SWE-bench Pro tasks (61.50%), gpt-5.4 follows at 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 newest frontier models (GPT-5.6, Fable 5, Opus 4.8, Kimi K3) are not on Scale's standardized board yet, so they appear on the independent SWE-bench Verified section instead. 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. Muse Spark 1.1 and gpt-5.4 top the standardized set, and gpt-5.4 is competitive on cost per point at $0.25. Haiku 4.5 solves about two thirds 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).

Frontend Code Arena: Kimi K3 Is #1, Ahead of Fable 5

SWE-bench measures repo-scale bug fixing on Python. It says almost nothing about the work most developers do daily: building UI. The Arena.ai Frontend Code Arena fills that gap with blind pairwise human votes on real frontend prompts, and in July 2026 the model on top is Kimi K3, the first open model to lead it, ahead of Claude Fable 5.

Kimi K3 (Moonshot AI) launched July 16, 2026 and took #1 on the Frontend Code Arena at roughly 1,679 points, with Claude Fable 5 second and GPT-5.6 Sol third. It is the first open model to top a frontend coding leaderboard, and on Vals AI's independent SWE-bench Verified harness it scores 93.4%, third behind GPT-5.6 Sol (96.2%) and Fable 5 (95.0%). The catch is serving: on Moonshot's own API, Artificial Analysis measures Kimi K3 at about 34 tokens per second with a 7-second time to first token, and the open weights are not out yet (a Hugging Face release is expected around July 27), so no third party can self-host it faster. That is the gap the Morph Kimi K3 API fills, serving the same model at about 100 tokens per second on an OpenAI-compatible endpoint.

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 Vals AI's independent SWE-bench Verified harness, GPT-5.6 Sol leads at 96.2%, then Claude Fable 5 (95.0%) and Kimi K3 (93.4%). Vals AI runs a single standardized harness across models, which makes it the cleaner Verified reference: the broader llm-stats board is entirely vendor self-reported (0 of 104 entries independently verified). SWE-bench Verified is older, Python-only, and partially contaminated, but it is still the number every launch post quotes. The independent top tier, then the wider vendor board:

The wider vendor-reported board (llm-stats, July 2026) fills in the tier below the frontier: Claude Opus 4.7 (87.6%), Claude Sonnet 5 (85.2%), then the open-weight 80-percent cluster: DeepSeek-V4-Pro-Max (80.6%), Gemini 3.1 Pro (80.6%), MiniMax M3 (80.5%), Qwen3.7 Max (80.4%), and Kimi K2.6 (80.2%). Two structural facts hold. First, the very top is a three-way race now: GPT-5.6 Sol, Fable 5, and Kimi K3 sit within 2.8 points on the independent harness, and Opus 4.8 at $5/$25 covers most teams below them. Second, the 80-percent cluster is mostly open weights; DeepSeek-V4-Pro-Max 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 live on the API (restored July 1 after a brief export-control suspension), 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-5.6 Sol: The Everyday Frontier Pair

GPT-5.6 Sol reached general availability on July 9, 2026 and is now OpenAI's top model on the API and through Codex. On Vals AI's independent SWE-bench Verified harness it leads at 96.2%, well ahead of Claude Opus 4.8 at 88.6%. Opus 4.8 answers on price and repo-scale engineering: it costs less output ($25 vs $30 per M) and leads SWE-bench Pro on vendor harnesses (69.2% vs Sol's 64.6%). The splits:

GPT-5.6 Sol is the raw-capability leader on the independent Verified harness and is what OpenAI now points Codex at: the dedicated -codex variants are retired, so the general gpt-5.6 is the current Codex model. Opus 4.8 wins repo-scale software engineering on vendor SWE-bench Pro (69.2% vs 64.6%) and is cheaper on output. If your work lives in a CLI agent, Codex pricing changes the math; if it lives in long-horizon repo edits at half the output price, Opus 4.8 does.

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

The open-model tier split three ways in July 2026. For frontend, Kimi K3 leads the Frontend Code Arena ahead of Fable 5 (weights due ~July 27). For value, GLM-5.2 is the top open model on the Artificial Analysis Intelligence Index, served on Morph at $1.10/$4.10. For raw SWE-bench Verified, DeepSeek-V4-Pro-Max is highest at 80.6% (vendor), tied with Gemini 3.1 Pro and self-hostable under MIT, with MiniMax M3 at 80.5% and Kimi K2.6 at 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 61.50% (Muse Spark 1.1). Both numbers are real. The difference is the harness: Scale runs every model through identical scaffolding; vendors run their own tuned agent stacks. Scale has not yet run the newest frontier models (Fable 5, Opus 4.8, GPT-5.6, Kimi K3), so their Pro numbers below are vendor-reported. (Vendor SWE-bench Verified numbers are also self-reported; llm-stats lists 0 of 104 as independently verified, which is why the Vals AI single-harness numbers above are the cleaner Verified reference.)

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 24, 2026):

• Vals AI SWE-bench Verified (independent single-harness scores: GPT-5.6 Sol, Fable 5, Kimi K3, Opus 4.8)
• Arena.ai Frontend Code Arena (blind pairwise human votes; Kimi K3 #1)
• 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 (Fable 5 $10/$50, Opus 4.8 $5/$25)
• OpenAI GPT-5.6 (GA July 9) and OpenAI API pricing
• Moonshot Kimi K3 pricing and Artificial Analysis Kimi K3 (throughput / TTFT)
• Google Gemini API pricing
• DeepSeek API pricing and Z.ai GLM-5.2 docs
• Artificial Analysis: GLM-5.2 leads the open-weights Intelligence Index
• Artificial Analysis Coding Agent Index methodology (cost per task, token usage per task)
• r/LocalLLaMA (practitioner threads on GLM-5.2 and Qwen 3.6 for local coding, mid-2026)