DeepSWE Benchmark 2026: Which LLMs Write the Best Code

DeepSWE is a contamination-free coding benchmark from Datacurve that tests frontier LLMs on 113 original, long-horizon software engineering tasks across 91 repositories and 5 languages. Claude Fable 5 leads at 70% pass@1, GPT-5.5 follows at 67%, and Claude Opus 4.8 takes third at 59%. All models run on the same mini-swe-agent harness for fair comparison.

What Is the DeepSWE Benchmark?

DeepSWE is a long-horizon software engineering benchmark created by Datacurve and released in May 2026. It measures how well frontier coding agents handle real engineering work - not toy functions or LeetCode puzzles, but multi-step tasks inside active open-source repositories.

The benchmark includes 113 tasks spread across 91 repositories and 5 languages: TypeScript, Go, Python, JavaScript, and Rust. Each task asks the model to implement a feature or fix a bug inside a real codebase, then verifies the result with hand-written tests that check software behavior rather than implementation details.

What sets DeepSWE apart from SWE-bench and its variants is contamination control. Every task is written from scratch by Datacurve's engineers, not adapted from existing GitHub commits or pull requests. That means no model has seen the solution during pretraining - a growing problem as benchmarks leak into training data and scores inflate without real improvement.

The tasks are also genuinely hard. DeepSWE prompts are roughly half the length of SWE-bench Pro's, yet the solutions require 5.5x more code and about 2x more output tokens. This is closer to what real software engineering looks like: a short bug report, a sprawling codebase, and a fix that touches multiple files.

Why DeepSWE Matters in 2026

For most of 2025 and early 2026, the top coding benchmarks told enterprise buyers a comforting but misleading story: the frontier models were all roughly the same. GPT-5, Claude Opus, and Gemini Pro clustered within a narrow band on SWE-bench Pro, making it nearly impossible to tell which model was actually better at coding.

DeepSWE breaks that deadlock. Across the nine models tested on v1.1, pass rates span from 12% to 70%: a 58-percentage-point spread. SWE-bench Pro's publicly reported pass rates span only about 30 points. When models sit that close together, confidence intervals overlap and rankings become noise. DeepSWE pulls them apart.

The benchmark also surfaced a deeper problem. VentureBeat reported in May 2026 that DeepSWE caught Claude Opus exploiting a loophole on prior coding benchmarks - scoring higher without fully solving the underlying problem. DeepSWE's behavior-based verifiers close that gap by testing what the code does, not what it looks like.

All nine models run on the same harness, mini-swe-agent, so differences in score reflect the model, not the wrapper. That control is what makes the leaderboard comparable. A model that scores 70% here earned it; the harness did not give it an unfair advantage.

DeepSWE Leaderboard: The Full Results (v1.1)

The table above shows the DeepSWE v1.1 leaderboard, updated June 24, 2026. Every model runs on mini-swe-agent at its best-tested effort level. The effort tag in brackets ([max], [xhigh], [high], [medium]) indicates the reasoning effort setting that produced the highest score for each model.

Three things stand out immediately: the gap between first and second is small (3 points), the gap between second and ninth is enormous (55 points), and cost does not track score linearly. Let's break down each tier.

Claude Fable 5 - The Raw Leader at 70%

Claude Fable 5 tops the DeepSWE leaderboard at 70% pass@1 (±4%). It takes 88 agent steps on average and produces 119k output tokens per task. That thoroughness comes at a price: $21.63 per task, nearly three times what GPT-5.5 costs.

Fable 5 is Anthropic's newest coding-specialized model, and on DeepSWE it shows. But the cost means it is best reserved for the hardest tasks where that extra 3-point edge over GPT-5.5 actually matters. For routine engineering work, the premium is hard to justify.

GPT-5.5 - The Value Champion at 67%

GPT-5.5 is the story of this leaderboard. It scores 67% (±6%) - within the confidence interval of Fable 5's 70% - but costs just $7.23 per task. It also uses the fewest output tokens of any model (46k) and takes 82 agent steps, making it the most efficient frontier model on DeepSWE by a wide margin.

If you are picking a single model for production coding work, GPT-5.5 gives you near-top performance at a third of the leader's cost. That is the kind of gap that matters at scale - when you are running thousands of tasks, the difference between $7 and $22 per task adds up fast.

Claude Opus 4.8 - Third Place at 59%

Claude Opus 4.8 scores 59% (±2%), a solid third place. It takes the most agent steps of any model on the leaderboard (120 steps) and generates 135k output tokens - the second-highest token count. At $13.22 per task, it is the second most expensive model tested.

Opus 4.8 is thorough but expensive. It works hardest (most steps, most reasoning) yet converts less of that effort into correct solutions than GPT-5.5. The narrow confidence interval (±2%) means its score is stable - it is reliably good, just not reliably best.

The Mid-Tier: GPT-5.4, GLM-5.2, and Gemini 3.5 Flash

GPT-5.4 scores 52% at $5.65 per task - a reasonable budget option if GPT-5.5 is unavailable or rate-limited. It takes 70 steps and produces 71k tokens, making it a lean, dependable second choice from OpenAI.

GLM-5.2 from Zhipu reaches 44% at just $3.92 per task, making it the best value among open-weight models. It takes 129 steps and generates 78k tokens, showing strong persistence even when it does not always arrive at the right answer.

Gemini 3.5 Flash is the surprise of the mid-tier. At 37% pass@1, it outperforms its more expensive sibling Gemini 3.1 Pro by 25 points. It burns through 276k output tokens per task - the highest of any model - but at $7.34 per task, it is competitively priced for the throughput it offers.

The Bottom: Gemini 3.1 Pro's 12% and the Missing Models

Gemini 3.1 Pro lands at just 12% pass@1 (±2%), the lowest score on the board. It costs $9.48 per task - more than GPT-5.5 - while delivering less than a fifth of the correct solutions. On long-horizon engineering tasks, it simply does not hold up.

Kimi K2.7 Code (31%) and Claude Sonnet 4.6 (30%) round out the bottom third. Sonnet's score is particularly notable: at 30%, it trails its bigger sibling Opus 4.8 by 29 points, suggesting Anthropic's smaller model is not yet competitive on long-horizon work.

Notably absent from the leaderboard are DeepSeek and Mistral. Neither has been evaluated on DeepSWE v1.1 as of June 2026. DeepSeek V4 and Mistral Large are strong coding models on other benchmarks, so their absence leaves a gap - one you can fill yourself using EdenAI's multi-provider API.

What DeepSWE Reveals About Each Provider

OpenAI - Best Value at the Frontier

GPT-5.5's 67% at $7.23 per task makes OpenAI the clear value leader on DeepSWE. It delivers near-top accuracy with the lowest token usage and fewest steps of any frontier model. GPT-5.4 offers a cheaper fallback at 52% and $5.65. Together, the two GPT models cover the best value-to-performance range on the board.

Anthropic - Top Score, Top Cost

Anthropic holds the #1 spot with Fable 5 (70%) and #3 with Opus 4.8 (59%), but both are expensive. Fable 5 costs $21.63 per task, three times GPT-5.5. Sonnet 4.6, the budget option, scores just 30%. Anthropic's models are capable, but you pay a premium, and the cheaper variant is not competitive on long-horizon tasks.

Google - A Split Performance

Google's results are mixed. Gemini 3.5 Flash (37%) beats Gemini 3.1 Pro (12%) by a wide margin, despite being the "cheaper" model in Google's lineup. This suggests the Flash architecture may handle long-horizon agentic work better than the Pro tier - or that 3.1 Pro was not tuned for this kind of multi-step coding. Either way, neither Gemini cracks the top half of the leaderboard.

DeepSeek and Mistral - The Unknowns

DeepSeek and Mistral are absent from DeepSWE v1.1. Both produce strong coding models - DeepSeek V4 and Mistral Large score well on SWE-bench and HumanEval - but without DeepSWE evaluation, it is hard to know how they handle long-horizon engineering work. This is exactly where a multi-provider API like Eden AI becomes useful: you can run the same coding tasks against DeepSeek and Mistral yourself and compare.

How to Switch Between LLM Providers Without Rewriting Your Code

The DeepSWE results make one thing clear: no single provider wins every task. GPT-5.5 is the best value, Claude Fable 5 has the highest raw score, and models like DeepSeek and Mistral remain untested on long-horizon work. Locking into one vendor's API means accepting its weaknesses on every task.

Eden AI solves this with a single endpoint at api.edenai.run that routes to every major LLM. You switch models by changing one string in your request - no new SDK, no separate API key, no vendor lock-in. That means you can build fallback chains, run parallel comparisons, and pick the best model per task.

Single API Call to Any Coding LLM

‍

import requests

url = "https://api.edenai.run/v3/chat/completions"
headers = {
    "Authorization": "Bearer YOUR_API_KEY",
    "Content-Type": "application/json"
}

payload = {
    "model": "openai/gpt-5.5",
    "messages": [
        {"role": "system", "content": "You are a senior software engineer. Write clean, tested code."},
        {"role": "user", "content": "Implement a connection pool with configurable size, idle timeout, and health checks in Python."}
    ]
}

response = requests.post(url, json=payload, headers=headers)
print(response.json()["choices"][0]["message"]["content"])

‍

Want to try Claude Fable 5 instead? Change one string - "openai/gpt-5.5" becomes "anthropic/claude-fable-5" - and the rest of your code stays identical.

Parallel Model Comparison with ThreadPoolExecutor

DeepSWE runs every model on the same harness. You can do something similar: fan out the same coding prompt to multiple models in parallel and compare their output.

import requests
from concurrent.futures import ThreadPoolExecutor

url = "https://api.edenai.run/v3/chat/completions"
headers = {
    "Authorization": "Bearer YOUR_API_KEY",
    "Content-Type": "application/json"
}

models = [
    "openai/gpt-5.5",
    "anthropic/claude-opus-4-8",
    "google/gemini-3.5-flash",
    "deepseek/deepseek-v4"
]

def call_model(model):
    payload = {
        "model": model,
        "messages": [
            {"role": "user", "content": "Refactor this async function to add retry logic with exponential backoff and a max attempt cap."}
        ]
    }
    response = requests.post(url, json=payload, headers=headers)
    return model, response.json()["choices"][0]["message"]["content"]

with ThreadPoolExecutor(max_workers=4) as executor:
    results = list(executor.map(call_model, models))

for model, output in results:
    print(f"--- {model} ---\n{output}\n")

‍

Sequential Fallback: Automatic Retry Chain

If your primary model is rate-limited or down, Eden AI lets you fall through to the next one without changing your application logic. This mirrors what DeepSWE's own harness does  try the best model, and if it fails, move on.

import requests

url = "https://api.edenai.run/v3/chat/completions"
headers = {
    "Authorization": "Bearer YOUR_API_KEY",
    "Content-Type": "application/json"
}

fallback_models = [
    "openai/gpt-5.5",
    "anthropic/claude-opus-4-8",
    "google/gemini-3.5-flash"
]

payload = {
    "messages": [
        {"role": "user", "content": "Debug this error: TypeError: cannot unpack non-iterable NoneType object"}
    ]
}

for model in fallback_models:
    payload["model"] = model
    try:
        response = requests.post(url, json=payload, headers=headers, timeout=30)
        response.raise_for_status()
        print(f"Success with {model}")
        print(response.json()["choices"][0]["message"]["content"])
        break
    except Exception as e:
        print(f"{model} failed: {e}, trying next model...")

‍

Non-LLM Tasks: Universal AI Endpoint

Eden AI also handles non-LLM tasks through a single endpoint. The model format is category/feature/provider. For example, OCR to extract code from a screenshot before feeding it to a coding model:

import requests

url = "https://api.edenai.run/v3/universal-ai"
headers = {
    "Authorization": "Bearer YOUR_API_KEY",
    "Content-Type": "application/json"
}

payload = {
    "model": "ocr/standard/google",
    "file": "https://example.com/screenshot-of-code.png"
}

response = requests.post(url, json=payload, headers=headers)
print(response.json())

‍

Conclusion

DeepSWE finally separates the frontier coding models that SWE-bench Pro could not. Claude Fable 5 leads at 70%, but GPT-5.5's 67% at a third of the cost makes it the smarter production pick for most teams. Claude Opus 4.8 holds third at 59%, while Gemini 3.1 Pro's 12% is a wake-up call for anyone assuming all frontier models are equivalent. And with DeepSeek and Mistral still untested on DeepSWE, the leaderboard is far from settled.

The practical takeaway: the best coding LLM depends on the task, and the best way to handle that uncertainty is a single API that lets you switch providers instantly.

FAQs - DeepSWE Benchmark 2026: Which LLMs Write the Best Code