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

In a comparison of DeepSeek V4 Pro 0813 and GPT-5.6 Sol on the DeepSWE benchmark, a cascade strategy using DeepSeek first and escalating to GPT-5.6 Sol solves 83.0% of tasks at $3.35 each, while GPT-5.6 Sol alone solves 72.7% at $8.37. DeepSeek V4 Pro 0813 is 35x cheaper per rollout ($0.24 vs $8.37) and achieves 88.5% pass@4 versus GPT-5.6 Sol's 85.8%, but GPT-5.6 Sol leads in pass@1 (72.7% vs 62.8%) and reliability (84.5% vs 71.0%).

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. In our DeepSeek V4 Pro 0813 https://www.together.ai/models/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. 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, textual-kitty-key-phases . So Pro does not add much new reach on top of Sol; what it adds is the ability to clear the shared tasks at minimal cost. Routing between them: the portfolio play Which is exactly why the cascade wins. Run DeepSeek V4 Pro 0813 first and escalate to Sol only when your test suite rejects the answer: 83.0% solved at \$3.35 per task. That is ten points above Sol alone 72.7% for less than half of Sol's own per-task price \$8.37 . The near-free first stage clears most of the queue, so Sol's premium price applies only to the hard remainder, and the tasks that reach Sol get a second independent attempt on top. The cascade even beats a perfect one-shot oracle router 80.8% , because two independent attempts beat one perfect pick. Accuracy is the same whichever model leads, but Pro-first is far cheaper \$3.35 vs \$8.44 , so always lead with the lower-cost model. What it means GPT-5.6 Sol is the single-model pick when correctness and latency both matter on the first try: highest pass@1, highest reliability, fastest and most concise, and six of eight domains. You pay 35x DeepSeek V4 Pro 0813 for that, and you must guardrail its 20% regression rate. DeepSeek V4 Pro 0813 is the value and best-of-k pick: near-flagship coverage, the higher four-shot ceiling, a cleaner failure profile, at \$0.24 a rollout. But the sharpest use of the pair is neither alone, it is Pro as Sol's front-end. A low-cost first stage that clears most of the work turns Sol into a cost you only pay on the tasks that need it, and buys flagship-beating coverage for under half of Sol's price. Data table: DeepSeek V4 Pro 0813 vs GPT-5.6 Sol, full results FAQs Is DeepSeek V4 Pro 0813 better than GPT-5.6 Sol? It depends on the metric. GPT-5.6 Sol wins single-attempt quality on DeepSWE pass@1 72.7% vs 62.8% , solves more tasks four-for-four 61 vs 35 , and is much faster per rollout. DeepSeek V4 Pro 0813 wins pass@4 88.5% vs 85.8% at 35x lower cost per rollout, so it is the stronger value pick for high-volume or retry-tolerant agent work. How much cheaper is DeepSeek V4 Pro 0813 than GPT-5.6 Sol? In our run, DeepSeek V4 Pro 0813 cost \$0.24 per rollout versus \$8.37 for GPT-5.6 Sol at max effort, roughly 35x cheaper. Measured per solved task, Pro returned 260 solves per \$100 against Sol's 9, about 30x the solved work per dollar. Which is better for coding, DeepSeek V4 Pro 0813 or GPT-5.6 Sol? GPT-5.6 Sol leads Python 74 , Go 79 , TypeScript 66 , and JavaScript 75 , and wins 6 of 8 task domains, led by data modeling and serialization at 92%. DeepSeek V4 Pro 0813 takes Rust 65 vs 60 and edges stateful reactivity 66 vs 64 . Sol is also the faster model per rollout; Pro reaches wider coverage across multiple attempts. Should I route between DeepSeek V4 Pro 0813 and GPT-5.6 Sol? Yes, if you can verify results. Running DeepSeek V4 Pro 0813 first and escalating to GPT-5.6 Sol when your test suite rejects the output reaches 83.0% at \$3.35 per task, beating Sol alone 72.7% at \$8.37 and a perfect one-shot oracle router 80.8% . Together the two cover 107 of 113 tasks. What is pass@k on DeepSWE? pass@k measures whether at least one of k attempts at a task passes the hidden test suite. pass@1 rewards getting it right first try; higher k rewards a model that can eventually reach a solution across several tries. DeepSeek V4 Pro 0813's edge grows as k increases.