{
  "name": "GENZ TECH AI Coding Leaderboard",
  "description": "The best AI models for coding, ranked by verified SWE-bench Verified & Pro scores plus pricing.",
  "url": "https://genztech.blog/ai-coding-leaderboard/",
  "updated": "2026-07-17",
  "updatedMeans": "The date the ranked data last actually changed, derived from this dataset's own revision history — not the date the file was last edited.",
  "sourcesChecked": "2026-07-17",
  "sourcesCheckedMeans": "The date we last re-ran the unranked models against an independent evaluation. Moves even when nothing changes; if it equals or trails `updated`, the board simply had no movement to report.",
  "metric": "SWE-bench Verified",
  "license": "https://creativecommons.org/licenses/by/4.0/",
  "attribution": "Data by GENZ TECH (https://genztech.blog/ai-coding-leaderboard/). Free to cite with attribution + a link.",
  "methodology": "We rank by SWE-bench Verified (500 real, human-validated GitHub issues resolved end-to-end), tiebroken by the harder SWE-bench Pro. A score is only printed once confirmed against an independent evaluation or the maker's primary source — and every row states which kind it is. Where both exist, we print both: one as the ranked score, the other in that row's note. We would rather show you the gap than ask you to trust our pick. Our independent reference is vals.ai, which runs every model itself through the same minimal bash-only harness (mini-swe-agent), so the models are compared on equal footing. That matters more than it sounds: SWE-bench scores a model and its scaffolding together, and vendors report using their own tuned scaffolds. Against vals.ai's neutral harness, the vendor claims on this board run 2.6 to 11.6 points optimistic. So rows marked vendor-reported are best-case numbers and are not strictly comparable to the independent ones — where we know the independent figure, we print it in the row's note. That is a deliberate choice and you should know we made it: on four rows (Claude Sonnet 5, MiniMax M3, Qwen3.7 Max, Kimi K2.6) an independent score for that exact model exists and is lower, and we still rank on the maker's published figure because it is the number that model is sold and quoted on. We disclose the independent one beside it instead of quietly restating the board on a single evaluator's harness choice. The honest consequence: positions that straddle the two regimes are approximate. Qwen3.7 Max is the sharpest case — it sits at #11 on Alibaba's 80.4%, and on the neutral harness its 68.8% would put it far down the table. Note that llm-stats, which we previously miscredited as an independent tracker, labels its own SWE-bench Verified table \"Verified: 0 / Self-reported: 104\"; it aggregates vendor claims. Models still being checked are marked “verifying” and shown without a number rather than estimated. Prices are per 1M input tokens on the standard API tier and can change — always confirm current pricing with the provider.",
  "models": [
    {
      "rank": 1,
      "name": "GPT-5.6 Sol",
      "maker": "OpenAI",
      "open": false,
      "sweBenchVerified": "96.2%",
      "sweBenchPro": null,
      "terminalBench": "88.8%",
      "inputPricePer1M": "$5",
      "outputPricePer1M": "$30",
      "context": null,
      "verified": true,
      "sameAs": []
    },
    {
      "rank": 2,
      "name": "Claude Fable 5",
      "maker": "Anthropic",
      "open": false,
      "sweBenchVerified": "95.0%",
      "sweBenchPro": "80.3%",
      "terminalBench": null,
      "inputPricePer1M": "$10",
      "outputPricePer1M": "$50",
      "context": "1M",
      "verified": true,
      "sameAs": [
        "https://www.anthropic.com/claude"
      ]
    },
    {
      "rank": 3,
      "name": "Claude Opus 4.8",
      "maker": "Anthropic",
      "open": false,
      "sweBenchVerified": "88.6%",
      "sweBenchPro": "69.2%",
      "terminalBench": "~82.7% (TB2.1)",
      "inputPricePer1M": "$5",
      "outputPricePer1M": "$25",
      "context": "1M",
      "verified": true,
      "sameAs": [
        "https://www.anthropic.com/claude"
      ]
    },
    {
      "rank": 4,
      "name": "Grok 4.5",
      "maker": "SpaceXAI (xAI)",
      "open": false,
      "sweBenchVerified": "86.6%",
      "sweBenchPro": "64.7%",
      "terminalBench": "83.3% (TB2.1)",
      "inputPricePer1M": "$2",
      "outputPricePer1M": "$6",
      "context": null,
      "verified": true,
      "sameAs": []
    },
    {
      "rank": 5,
      "name": "Claude Sonnet 5",
      "maker": "Anthropic",
      "open": false,
      "sweBenchVerified": "85.2%",
      "sweBenchPro": "63.2%",
      "terminalBench": "80.4% (TB2.1)",
      "inputPricePer1M": "$2",
      "outputPricePer1M": "$10",
      "context": "1M",
      "verified": true,
      "sameAs": [
        "https://www.anthropic.com/claude"
      ]
    },
    {
      "rank": 6,
      "name": "GPT-5.5",
      "maker": "OpenAI",
      "open": false,
      "sweBenchVerified": "82.6%",
      "sweBenchPro": "58.6%",
      "terminalBench": "82.7% (TB2.0)",
      "inputPricePer1M": "$5",
      "outputPricePer1M": "$30",
      "context": null,
      "verified": true,
      "sameAs": [
        "https://openai.com/"
      ]
    },
    {
      "rank": 7,
      "name": "Muse Spark 1.1",
      "maker": "Meta",
      "open": false,
      "sweBenchVerified": "82.0%",
      "sweBenchPro": null,
      "terminalBench": null,
      "inputPricePer1M": "$1.25",
      "outputPricePer1M": "$4.25",
      "context": "1M",
      "verified": true,
      "sameAs": []
    },
    {
      "rank": 8,
      "name": "DeepSeek V4 Pro",
      "maker": "DeepSeek",
      "open": true,
      "sweBenchVerified": "80.6%",
      "sweBenchPro": "55.4%",
      "terminalBench": "67.9% (TB2.0)",
      "inputPricePer1M": "$0.435",
      "outputPricePer1M": "$0.87",
      "context": "1M",
      "verified": true,
      "sameAs": [
        "https://www.deepseek.com/",
        "https://huggingface.co/deepseek-ai"
      ]
    },
    {
      "rank": 9,
      "name": "Gemini 3.1 Pro",
      "maker": "Google DeepMind",
      "open": false,
      "sweBenchVerified": "80.6%",
      "sweBenchPro": "54.2%",
      "terminalBench": null,
      "inputPricePer1M": "$2",
      "outputPricePer1M": "$12",
      "context": null,
      "verified": true,
      "sameAs": [
        "https://deepmind.google/models/gemini/"
      ]
    },
    {
      "rank": 10,
      "name": "MiniMax M3",
      "maker": "MiniMax",
      "open": true,
      "sweBenchVerified": "80.5%",
      "sweBenchPro": "59.0%",
      "terminalBench": "66.0 (TB2.1)",
      "inputPricePer1M": "$0.60",
      "outputPricePer1M": null,
      "context": "1M",
      "verified": true,
      "sameAs": [
        "https://www.minimax.io/"
      ]
    },
    {
      "rank": 11,
      "name": "Qwen3.7 Max",
      "maker": "Alibaba",
      "open": false,
      "sweBenchVerified": "80.4%",
      "sweBenchPro": "60.6%",
      "terminalBench": "69.7 (TB2.0)",
      "inputPricePer1M": "$1.25",
      "outputPricePer1M": "$3.75",
      "context": "1M",
      "verified": true,
      "sameAs": [
        "https://qwenlm.github.io/"
      ]
    },
    {
      "rank": 12,
      "name": "Kimi K2.6",
      "maker": "Moonshot AI",
      "open": true,
      "sweBenchVerified": "80.2%",
      "sweBenchPro": "58.6%",
      "terminalBench": null,
      "inputPricePer1M": "$0.95",
      "outputPricePer1M": "$4",
      "context": null,
      "verified": true,
      "sameAs": [
        "https://huggingface.co/moonshotai"
      ]
    },
    {
      "rank": 13,
      "name": "Gemini 3.5 Flash",
      "maker": "Google DeepMind",
      "open": false,
      "sweBenchVerified": "78.8%",
      "sweBenchPro": null,
      "terminalBench": null,
      "inputPricePer1M": "$1.50",
      "outputPricePer1M": "$9",
      "context": null,
      "verified": true,
      "sameAs": [
        "https://deepmind.google/models/gemini/"
      ]
    },
    {
      "rank": null,
      "name": "Kimi K3",
      "maker": "Moonshot AI",
      "open": true,
      "sweBenchVerified": null,
      "sweBenchPro": null,
      "terminalBench": "88.3%",
      "inputPricePer1M": "$3",
      "outputPricePer1M": "$15",
      "context": "1M",
      "verified": false,
      "sameAs": []
    },
    {
      "rank": null,
      "name": "LongCat-2.0",
      "maker": "Meituan",
      "open": true,
      "sweBenchVerified": null,
      "sweBenchPro": "59.5%",
      "terminalBench": "70.8%",
      "inputPricePer1M": "$0.30",
      "outputPricePer1M": null,
      "context": "1M",
      "verified": false,
      "sameAs": []
    },
    {
      "rank": null,
      "name": "Cohere North Mini Code",
      "maker": "Cohere",
      "open": true,
      "sweBenchVerified": null,
      "sweBenchPro": null,
      "terminalBench": null,
      "inputPricePer1M": "Free",
      "outputPricePer1M": "Free",
      "context": "256K",
      "verified": false,
      "sameAs": []
    },
    {
      "rank": null,
      "name": "KAT-Coder-Pro V2.5",
      "maker": "Kwaipilot (Kuaishou)",
      "open": false,
      "sweBenchVerified": null,
      "sweBenchPro": "65.2%",
      "terminalBench": null,
      "inputPricePer1M": "$0.74",
      "outputPricePer1M": "$2.96",
      "context": "256K",
      "verified": false,
      "sameAs": []
    }
  ]
}