TokenMix Research Lab · 2026-04-20

SWE-Bench 2026: Claude Opus 4.7 Wins 87.6% vs GPT-5.3 85.0%

SWE-Bench Verified leaderboard as of April 2026 (Local AI Master 2026 leaderboard, llm-stats) has a clear top-three: Claude Opus 4.7 at 87.6%, GPT-5.3-Codex at 85.0%, Gemini 3.1 Pro at 80.6%. The surprise entries: Qwen3.6 Plus (Alibaba, April 2026) at 78.8% and Muse Spark (Meta) at 77.4% closed the open-weights gap to single digits. On the harder SWE-Bench Pro (Scale AI), Claude Opus 4.7 still leads at 64.3% but all models drop ~20 points — showing how much of "Verified" performance was benchmark-specific. TokenMix.ai exposes every leaderboard model through one OpenAI-compatible endpoint so engineering teams can A/B coding models on real PRs without SDK migration.

Table of Contents

• SWE-Bench Verified Leaderboard: Full Results April 2026
• SWE-Bench Pro: The Harder Benchmark
• What SWE-Bench Actually Measures
• Why the Pro Gap Matters
• Cost Math: $ per Successful Fix by Model
• Beyond Benchmarks: Real-World Coding Agent Reality
• How to Choose a Coding Model in 2026
• Conclusion
• FAQ

SWE-Bench Verified Leaderboard: Full Results April 2026

Opus 4.7 is the first model to cross 87% on Verified, shipping with 1M context and tightened training for multi-file patches. The 2.6-point lead over GPT-5.3-Codex sounds small but translates to roughly 10-12 additional successful fixes per 500 tasks — material at production scale.

SWE-Bench Pro: The Harder Benchmark

SWE-Bench Pro (Scale AI, late 2025) addresses the criticism that SWE-Bench Verified had become partially solved through benchmark-specific optimization. Pro raises the bar with harder tasks, more files per patch, and fewer hints.

Top results (Pro):

• Claude Opus 4.7: 64.3% (Anthropic-reported, April 2026)
• GPT-5.4 (xHigh, Scale SEAL mini-swe-agent): 59.1%
• Gemini 3.1 Pro: ~54%
• Qwen3.6 Plus: ~52%

Every top model drops 18-25 points from Verified to Pro. That's not because models got worse — it's because Verified underestimated the remaining difficulty of real software engineering.

What SWE-Bench Actually Measures

SWE-Bench asks a model to resolve a real GitHub issue by producing a patch that passes the repository's existing test suite. The model is given: the issue text, the relevant repository at the pre-patch commit, and typically a failing test.

What it captures well:

• Multi-file understanding (real issues touch 2-5 files on average)
• Test-driven correctness (the fix must pass tests the model never saw during training)
• Framework knowledge (Django, Flask, Pytest, Scikit-learn, etc.)

What it doesn't capture:

• Architecture decisions (SWE-Bench patches are scoped to existing structure)
• Long-horizon tasks (each issue is typically a bounded fix, not a multi-week feature)
• Interactive debugging (no runtime experimentation across hours)
• Code review judgment (only pass/fail on tests, no style or maintainability)

So 87.6% on Verified does not mean 87.6% of your engineering work can be automated. It means the model handles roughly nine of ten well-scoped bug fixes when given perfect task specification.

Why the Pro Gap Matters

The 20-point Verified-to-Pro drop tells you something important: a chunk of benchmark progress in 2024-2025 was scaffolding and prompt engineering specific to Verified's patterns, not general improvement in code reasoning.

Models that lead on Pro have more general capability. In April 2026, Claude Opus 4.7 leads both, which is the strongest signal that its SWE performance generalizes.

Implication for production: trust Pro scores more than Verified for real-world code generation quality. A model that scores 80% on Verified but 50% on Pro is benchmark-overfit. A model that scores 85/60 is more trustworthy.

Cost Math: $ per Successful Fix by Model

For a typical SWE-Bench task, models consume 30K-80K tokens across reasoning, patch generation, and test runs. Take the midpoint (55K) for cost estimation.

Qwen3.6 Plus and DeepSeek V4 offer the cheapest cost-per-successful-fix, roughly 7× cheaper than Opus 4.7 while only 10-12 points behind on success rate. For production code-fixing pipelines where success rate differences of 7-10% are tolerable, the cheap options dominate on dollars per merged PR.

The practical routing play: use Opus 4.7 for hard tasks where success rate matters, fall back to Qwen3.6 Plus or DeepSeek V4 for bulk bug fixes. Route through TokenMix.ai to switch models per-task based on complexity classifiers.

Beyond Benchmarks: Real-World Coding Agent Reality

Three things SWE-Bench doesn't predict:

1. Codebase scale. Most SWE-Bench repos are 10K-100K LOC. Production codebases are 100K-10M LOC. Models with 1M context (Opus 4.7, Gemini) handle large repos better regardless of their benchmark numbers.

2. Multi-model agent scaffolding. Production systems like Cursor, Claude Code, and Aider stack model calls with tool use, retrieval, and human-in-the-loop review. A 3-point benchmark gap often disappears when both models run inside the same scaffolding.

3. Cost sensitivity. Developers working interactively care about latency (first edit in <5 seconds) as much as quality. Benchmarks don't measure latency.

How to Choose a Coding Model in 2026

Conclusion

Claude Opus 4.7 is the April 2026 leader on both SWE-Bench Verified (87.6%) and Pro (64.3%), marking the first model to clearly dominate both. GPT-5.3-Codex and Gemini 3.1 Pro are within reach and cheaper. Qwen3.6 Plus and DeepSeek V4 are the pragmatic choices when cost-per-successful-fix dominates the decision.

Benchmark rank isn't destiny — production agents stack model calls with tools, retrieval, and review. Run the models that matter on your real repository to measure what actually ships. TokenMix.ai makes that A/B trivial: one API key, every leaderboard model, no SDK migration.

FAQ

Q1: What is the top SWE-Bench Verified score in April 2026?

Claude Opus 4.7 at 87.6%, released April 16, 2026 by Anthropic with 1M context. Second is GPT-5.3-Codex at 85.0%. Third place is a tie-ish cluster of Claude Opus 4.5 (80.9%), Opus 4.6 (80.8%), and Gemini 3.1 Pro (80.6%).

Q2: Why are SWE-Bench Pro scores lower than Verified?

SWE-Bench Pro was designed to resist benchmark-specific optimization that had partially inflated Verified scores. Pro uses harder tasks, more files per patch, fewer hints. Every top model drops 18-25 points from Verified to Pro — the gap reflects how much Verified optimization was benchmark-specific rather than general improvement.

Q3: Is Claude Opus 4.7 worth the cost premium for coding?

For maximum quality on hard tasks, yes. Opus 4.7 is the only model that leads both Verified and Pro, suggesting its performance generalizes. For high-volume, cost-sensitive workloads, Qwen3.6 Plus or DeepSeek V4 deliver 7-10 points less success at one-seventh the cost.

Q4: Which open-weights model is best for coding in 2026?

Qwen3.6 Plus (Alibaba, April 2026) leads at 78.8% on SWE-Bench Verified among open-weights models, closely followed by Muse Spark (Meta) at 77.4%. Both are within 9-10 points of the closed-model leaders — the open-closed gap has narrowed significantly.

Q5: How much does it cost to fix a bug with each top model?

Approximate cost per successful fix on SWE-Bench-scale tasks: Opus 4.7 .71, GPT-5.3-Codex .06, Gemini 3.1 Pro $0.93, Qwen3.6 Plus $0.25, DeepSeek V4 $0.23. Bulk fix pipelines favor the cheap tail; high-stakes fixes favor the premium tier.

Q6: Do SWE-Bench scores translate to real-world engineering productivity?

Partially. SWE-Bench captures well-scoped bug fixes with clear test-based success criteria. Real engineering includes architecture decisions, long-horizon feature work, and interactive debugging — none measured by SWE-Bench. Use SWE-Bench as a signal, not a complete productivity predictor.

Q7: How do I pick between Claude Opus 4.7 and GPT-5.3-Codex?

On SWE-Bench the gap is 2.6 points favoring Opus 4.7. The practical deciders are: (1) do you need 1M context for large repos (pick Opus); (2) is cost a factor (GPT-5.3-Codex ~40% cheaper per call); (3) are you already tooled on one (stay where scaffolding works). Route both through TokenMix.ai to A/B on real PRs.

Sources

• SWE-bench Official Site — benchmark definition and viewer
• SWE-bench Results Viewer — task-level results
• Vals.ai — SWE-bench Benchmark — independent tracker
• Epoch AI — SWE-bench Verified — longitudinal scoring data
• Scale AI — SWE-Bench Pro Leaderboard (Public Dataset) — Pro benchmark official rankings
• Local AI Master — SWE-Bench 2026: Claude 77.2% vs GPT-5 74.9% Full Leaderboard — April 2026 ranking snapshot
• Local AI Master — Best AI Coding Models 2026 — cross-benchmark evaluation
• llm-stats — SWE-Bench Verified Leaderboard — continuously updated rankings
• Marco Patzelt — SWE-Bench Verified Leaderboard March 2026 — developer-maintained tracker
• DEV Community — SWE-bench Scores and Leaderboard Explained (2026) — methodology walkthrough

Data collected 2026-04-20. SWE-Bench rankings update weekly; this article reflects the Apr 16-20 snapshot. For model selection decisions, check the official leaderboard for the latest scores.

By TokenMix Research Lab · Updated 2026-04-20