DeepSeek V4 Pro 0813 vs GPT-5.6 Sol on DeepSWE: Cost, Coding, and Routing

DeepSeek V4 Pro 0813 vs GPT-5.6 Sol on DeepSWE: Cost, Coding, and Routing Webflow Analyze/Optimize tracking bridge -->

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Published 8/18/2026

DeepSeek V4 Pro 0813 vs GPT-5.6 Sol on DeepSWE: Cost, Coding, and Routing

We ran 904 DeepSWE rollouts. The winner was neither model on its own: Pro first, Sol on failure, 83.0% at $3.35 a task.

Authors

Zain Hasan, Shobhit Dixit

Table of contents

40+ Models Chosen for Production...40+ Models Chosen for Production...40+ Models Chosen for Production...

Key Takeaways

Don't pick one. Run DeepSeek V4 Pro 0813 first, escalate to GPT-5.6 Sol when the tests fail. That cascade solves 83.0% of DeepSWE tasks at \$3.35 each. Sol alone solves 72.7% at \$8.37. Ten points better, 60% cheaper. Sol wins the early attempts. 72.7% pass@1 vs 62.8%, and it holds the lead at pass@2 (81.0% vs 78.5%). Pro wins the last one. 88.5% pass@4 vs 85.8%. Given four tries, the cheap model finds more of the board. The price gap is 35x. \$0.24 per rollout vs \$8.37. Per \$100 spent, Pro solves 261 tasks and Sol solves 9. Sol is faster and steadier. 17 minutes and 53 steps per rollout vs Pro's 35 and 146, and 61 tasks solved four-for-four vs 35. Sol's failures are messier. 20% of them break tests that already passed, vs 11% for Pro. Gate Sol's output on regressions.

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‍ In our DeepSeek V4 Pro 0813 vs GPT-5.6 Sol comparison on DeepSWE, a benchmark that tests a model's software engineering ability across many task types and programming languages, GPT-5.6 Sol is the best single-shot engineer on the board, and DeepSeek V4 Pro 0813 costs one-thirty-fifth as much. The interesting question is not which is more accurate on the first try (Sol, clearly) but what that 35x price gap actually buys, and whether the lower-cost model's ceiling closes it. DeepSWE · Head to Head

DeepSeek-V4 Pro 0813 vs GPT-5.6 Sol at a glance

Model Pass@1 Avg cost Solves / $100 Out tok Steps

gpt-5.6-sol [max] 72.7% ± 2.2% $8.37 9 59k 53

deepseek-v4-pro-0813 [max] 62.8% ± 3.1% $0.24 260 101k 146

We ran DeepSeek V4 Pro 0813 (max) against GPT-5.6 Sol (max) on all 113 DeepSWE tasks, four trials each, from the published per-trial records: 904 rollouts in total (452 each). Sol is the precision flagship; Pro is the value outlier. Every figure below comes from this run, so it can differ from other public DeepSeek V4 Pro 0813 vs GPT-5.6 Sol scorecards.

The DeepSWE scoreboard: pass@1 and pass@k Single shot, Sol leads clearly: 72.7% pass@1 to Pro's 62.8% (official scoring). But the gap narrows with every retry and then inverts: at two attempts Pro is already close (78.5 vs 81.0), and at four Pro's 88.5% pass@4 passes Sol's 85.8%. The lower-cost model has the wider reach; it needs more than one attempt, and its attempts are nearly free. If your workload lets you run best-of-k, the accuracy argument for Sol largely disappears.

Cost comparison: DeepSeek V4 Pro 0813 vs GPT-5.6 Sol pricing At \$0.24 a rollout, DeepSeek V4 Pro 0813 is 35x cheaper than Sol (\$8.37), which in value terms is 260 solves per \$100 against Sol's 9. What the lower price does not buy is speed or brevity: Pro takes a median 146 steps and 35 minutes and emits 101k output tokens, versus Sol's tight 53 steps, 17 minutes, and 59k tokens. Sol is the fast, concise specialist; Pro reaches similar coverage the long way around. If a human is waiting, Sol earns its premium on latency alone; if a budget or a queue is waiting, nothing here is close to Pro.

Coverage vs reliability: precision vs reach Decompose pass@1 into coverage and reliability and the split is clean. Sol is the precision corner: 84.5% reliability and 61 tasks solved four-for-four, the marks of a model that nails what it touches. DeepSeek V4 Pro 0813 trades to the other axis: wider coverage (88.5% vs 85.8%) but far lower reliability (71.0%) and fewer solid tasks (35 vs 61). That is the pass@4 crossover seen from the side: Pro touches more of the benchmark than Sol but converts each touch less often per shot. Failure modes: how each model gets it wrong The failure profiles differ sharply. Sol breaks the repository's existing test suite in 20% of its failures, the GPT-family regression signature. DeepSeek V4 Pro 0813 is far more conservative at 11%; when it fails, it usually fails with a near miss and the baseline intact. So the cheaper model is also the safer one to accept unreviewed: put a full regression gate around Sol before you take its diff, and Pro needs that guardrail less.

Where each wins, by task type Sol's quality edge is broad: it wins 6 of 8 domains, led by data modeling and serialization at 92% (28 points over Pro), plus query/config (80), concurrency (72), build/ops (73), program analysis (64), and protocol conformance (59). The two it does not take are narrow: language and runtime internals is a 75-75 tie, and DeepSeek V4 Pro 0813 edges stateful reactivity (66 vs 64), its one domain win. Everywhere the task demands nailing an exact serialization contract, Sol pulls away.

DeepSeek V4 Pro 0813 vs GPT-5.6 Sol by programming language Sol sweeps four of five (Python 74, Go 79, TypeScript 66, JavaScript 75), and the margins are wide on Python and Go. The exception is Rust, where DeepSeek V4 Pro 0813 edges ahead 65 to 60: the one language Sol underperforms its class and the one Pro wins outright. If your stack is Python or Go, this is Sol's board by a comfortable margin; if it is Rust, Pro is fractionally better and vastly cheaper.

How different are DeepSeek V4 Pro 0813 and GPT-5.6 Sol? Moderately. Per-task correlation is 0.54, the closest of the DeepSeek-Pro pairings. They both solve 90 tasks; Pro alone gets 10, Sol alone gets 7, and 6 defeat both. Their union covers 107 of 113 (94.7%). One asymmetry matters for a portfolio: DeepSeek V4 Pro 0813 sweeps no task that Sol misses entirely, while Sol goes four-for-four on two tasks that Pro never solves (pebble-durability-wait-apis,...