OpenAI shipped GPT-5.6 in three flavors, Sol, Terra and Luna, in July 2026. DeepSeek answered on August 13 with V4 Pro 0813, an open-weight update that jumped its own Vals Index score by 9.48 points in a single release. Both labs are now fighting over the same territory: developers who want a model that can plan a multi-file refactor, run it in a terminal, and not blow the monthly API bill doing it. The gap between them is no longer about raw intelligence. It is about price, openness, and which specific benchmark you care about most.
This comparison pulls numbers straight from OpenAI’s own launch tables, DeepSeek’s official pricing docs, and third-party trackers like Vals.ai and Artificial Analysis, so you can see exactly where GPT-5.6 wins, where DeepSeek V4 Pro 0813 wins, and where the two are close enough that price should decide it for you. We cover coding benchmarks, agentic tool use, pricing down to the token, context windows, five real-world use cases, and a step-by-step migration path if you are moving workloads from one to the other.
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GPT-5.6 and DeepSeek V4 Pro 0813 at a Glance
GPT-5.6 is OpenAI’s closed-weight flagship family, split into three reasoning tiers that share the same 1.05-million-token context window but differ sharply in price and depth of reasoning. Sol is the top tier built for the hardest coding and agentic tasks. Terra is the balanced mid-tier for general enterprise workloads. Luna is the cheap, fast tier meant for high-volume, lower-complexity requests. All three launched together in July 2026 and got a further price cut on Terra and Luna on July 30, according to a post on the OpenAI developer community forum.
DeepSeek V4 Pro 0813, by contrast, is a single open-weight model, a refresh of the earlier DeepSeek V4 Pro release, dated to its August 13 build number. Under the hood it is a mixture-of-experts model with 1.6 trillion total parameters and 49 billion active parameters per forward pass, a configuration DeepSeek first previewed back in April 2026 before the 0813 refresh. It is DeepSeek’s flagship reasoning and coding model, sitting above the cheaper V4 Flash tier the same way GPT-5.6 Sol sits above Luna. Where GPT-5.6 is API-only and fully closed, DeepSeek V4 Pro 0813 ships weights that developers can download, self-host, or run through third-party inference providers like OpenRouter, which changes the cost math entirely for teams with their own GPU capacity.
The headline number most people search for first is price. GPT-5.6 Sol costs $5.00 per million input tokens and $30.00 per million output tokens on OpenAI’s standard API, according to OpenAI’s own pricing page. DeepSeek V4 Pro 0813’s official docs list off-peak cache-hit input at just $0.022 per million tokens, a gap of roughly 227 times on that specific line item. That gap narrows a lot once you look at cache misses, peak-hour pricing, and output tokens, which we break down fully in the pricing section below.
Full Specs Comparison Table
Here is a direct, spec-by-spec look at GPT-5.6 Sol, the tier most directly comparable to DeepSeek’s flagship, against DeepSeek V4 Pro 0813. Where relevant we also note Terra and Luna, since most teams end up choosing a GPT-5.6 tier rather than defaulting straight to Sol.
| Spec | GPT-5.6 Sol | DeepSeek V4 Pro 0813 |
|---|---|---|
| Release date | July 2026 (GA), priced flat through August | August 13, 2026 |
| Weights | Closed, API-only | Open-weight, self-hostable |
| Context window | ≈1.05M tokens | 1.0M tokens |
| Input price (per 1M tokens) | $5.00 | $0.022 cache-hit off-peak / $0.66 cache-miss off-peak |
| Output price (per 1M tokens) | $30.00 | $1.98 off-peak / $3.96 peak |
| Cached input price | $0.50 per 1M | Built into cache-hit tier above |
| SWE-Bench Pro | 64.6% | Not published on this benchmark |
| SWE-bench Verified | Not published for Sol specifically | 96.40% (#2 of 82 models tracked) |
| Terminal-Bench 2.1 | 88.8% | 54.68% |
| BrowseComp | 90.4% | Not published |
| Artificial Analysis Coding Agent Index v1.1 | 80 (max reasoning) | Not ranked in this specific index |
| Vals Index overall score | Not tracked on Vals under this exact metric | 52.37% (#18 overall) |
| Peak/off-peak pricing | No, flat rate | Yes, peak hours 01:00-04:00 and 06:00-10:00 UTC |
| Self-hosting option | No | Yes, via downloaded weights |
A few things jump out immediately. GPT-5.6 Sol and DeepSeek V4 Pro 0813 rarely get scored on the exact same benchmark in official launch materials, which is exactly why so many comparison trackers exist to fill that gap. When the two do overlap, on Terminal-Bench 2.1, GPT-5.6 Sol’s 88.8% badly outpaces DeepSeek’s 54.68%. But on SWE-bench Verified, where DeepSeek V4 Pro 0813 hits 96.40% and ranks as the highest-scoring open-weight model on the Vals.ai leaderboard, GPT-5.6’s comparable Sol number is not published in the same table, which itself tells you something about which benchmark each lab chose to spotlight.
Coding Benchmarks: SWE-Bench, Terminal-Bench and DeepSWE
Coding is where both companies are spending their marketing budget, so it is worth separating the benchmarks carefully instead of treating every “SWE-bench” number as interchangeable. SWE-bench Verified is the older, more saturated test, where top models now regularly clear 90%. SWE-Bench Pro is a newer, harder successor built specifically because Verified stopped separating frontier models from each other.
On SWE-Bench Pro, GPT-5.6’s launch table shows Sol at 64.6%, Terra at 63.4%, and Luna at 62.7%, each a clear step up from GPT-5.5’s 59.4% on the same configuration, according to OpenAI’s own published table. A separate analysis from GoML points out something OpenAI’s launch post does not emphasize: at 64.6%, GPT-5.6 Sol still trails Anthropic’s Claude Opus 4.8, Fable 5 and Mythos 5 on that specific benchmark. So GPT-5.6 is a real generational jump over GPT-5.5, but it is not the outright SWE-Bench Pro leader across every lab.
DeepSeek V4 Pro 0813 does not appear in OpenAI’s own SWE-Bench Pro table, but independent trackers have since filled that gap: China AI Bench’s August 2026 review puts V4-Pro (max) at 55.4% on SWE-Bench Pro and 80.6% on SWE-bench Verified, and MacGPU’s July 2026 benchmark run separately clocked V4-Pro at 80.6% on SWE-bench Verified and 93.5% on LiveCodeBench Pass@1. Those numbers sit well below Vals.ai’s headline 96.40% SWE-bench Verified figure, which is a reminder that DeepSeek’s score depends heavily on which tracker and which exact build you cite. Vals.ai still has V4 Pro 0813 at 96.40%, good for second place out of 82 tracked models and the single highest score among every open-weight model on the board, ahead of Kimi K3’s 93.40%. DeepSeek’s own changelog backs that up with two internal benchmarks for the V4 family: a DSBench-FullStack score of 71.1 and a DSBench-Hard score of 67.2, both published in the model’s official API docs as of August 2026. That is a meaningful claim: it means for a huge share of realistic bug-fix and small-feature tasks, an open-weight model you can download and run yourself is now outperforming almost every closed frontier model tracked on that leaderboard.
Terminal-Bench 2.1 is where the two diverge hardest. This benchmark tests a model’s ability to actually operate a terminal, chain shell commands, debug failures live, and recover from mistakes, which is a rough proxy for how a model performs inside an agentic coding tool like a CLI assistant. GPT-5.6 Sol scores 88.8%, Terra 87.4%, and Luna 84.7%, all ahead of GPT-5.5’s 85.6%. DeepSeek V4 Pro 0813 scores 54.68% on the same benchmark, ranking 33rd of 52 models tracked by Vals.ai, and dropping to 28.89% on the hardest subset of tasks. If your workload leans heavily on multi-step terminal automation rather than single-file code generation, this is the single biggest data point in GPT-5.6’s favor.
Agentic and Tool-Use Performance
Coding accuracy is only half the story for anyone building agents. The other half is tool orchestration: can the model call the right API, chain multiple tools without losing state, and browse the web to fill gaps in its own knowledge. GPT-5.6’s BrowseComp score, a benchmark for browsing-assisted reasoning, has Sol at 90.4%, Terra at 87.5%, and Luna at 83.3%, each beating GPT-5.5’s 84.4%. That is a solid improvement in the model’s ability to search, read, and synthesize information from live web pages rather than relying purely on training data.
On the Artificial Analysis Coding Agent Index v1.1, a composite score meant to reflect real agentic coding performance rather than a single static benchmark, GPT-5.6 Sol running at max reasoning effort scores 80, which Artificial Analysis reports as 2.8 points higher than Anthropic’s Claude Fable 5 on the same index. That index does not currently carry a published DeepSeek V4 Pro 0813 score, so a direct number-to-number comparison on this specific metric is not possible with the sources available. It is worth flagging that gap in coverage rather than papering over it, since DeepSeek’s own team has instead published agentic numbers for its cheaper V4 Flash sibling, not V4 Pro 0813 itself, on benchmarks like DeepSWE, Cybergym and Toolathlon.
What DeepSeek V4 Pro 0813 does have published, from the Vals Index, is a set of large jumps over the prior non-Pro DeepSeek V4 baseline in reasoning-heavy tasks. ProofBench moved from 10.00% to 49.00%, taking DeepSeek from rank #45 to #15 among tracked models. Legal Research Bench moved from 23.08% to 40.87%, a jump from #27 to #11. Those are not agentic tool-use benchmarks in the BrowseComp sense, but they show DeepSeek’s team focused this release heavily on reasoning depth rather than just raw coding throughput.
Reasoning, Science and Legal Benchmarks
Outside of coding, GPT-5.6 picked up targeted gains in domain-specific reasoning that matter for research and healthcare-adjacent use cases. On LifeSciBench, GPT-5.6 Sol hits 59.9%, well ahead of GPT-5.5’s 50.4% on the same evaluation table. On MedChemBench, Sol reaches 48.3% against GPT-5.5’s 35.5%. On HealthBench, a safety-oriented benchmark tracked by OpenAI’s own deployment safety hub, GPT-5.6 Sol scores 55.0, ahead of GPT-5.5 Instant’s 51.4 and GPT-5.3 Instant’s 49.6. None of these have a directly published DeepSeek V4 Pro 0813 equivalent in the sources reviewed for this comparison, so we cannot claim a head-to-head winner in life sciences or medical chemistry specifically. What we can say is that OpenAI clearly prioritized these domains in the GPT-5.6 training and evaluation cycle.
DeepSeek’s strongest published reasoning gains, by contrast, cluster around legal and formal-proof tasks, plus general knowledge breadth. The Vals Index puts DeepSeek V4 Pro 0813’s overall score at 52.37%, a jump of 9.48 points over the prior DeepSeek V4 baseline’s 42.89%, moving it to #18 overall on that leaderboard. On the narrower BenchLM DeepSeek-only leaderboard, updated August 19, 2026, V4 Pro 0813 is ranked the top DeepSeek model with a score of 61.3, ahead of every earlier DeepSeek release including V4, V3 and the Flash variants.
One useful sanity check comes from DeepSeek’s own comparison of the base V4 model, not the Pro 0813 variant, against rivals: DeepSeek V4 scored above 80% on SWE-bench Verified, close to GPT-5.4’s roughly 80% and behind Claude Opus 4.8’s 88.6%, while the older DeepSeek V3 sat around 49% on the same test. That baseline shows how far the V4 family has come in a single generation, and it puts the newer V4 Pro 0813’s 96.40% SWE-bench Verified score in context: it is not just an incremental patch, it is a substantial jump even over DeepSeek’s own prior flagship.
Generation-Over-Generation: How Much Did Each Model Actually Improve?
It helps to step back and ask how much of this is genuine progress versus marketing framing around a fresh version number. On the OpenAI side, the jump from GPT-5.5 to GPT-5.6 is consistent and measurable across every benchmark both models share. SWE-Bench Pro moved from 59.4% to 64.6% for the top tier, a 5.2-point gain. Terminal-Bench 2.1 moved from 85.6% to 88.8%, a 3.2-point gain. BrowseComp moved from 84.4% to 90.4%, a 6-point gain, the largest single-benchmark jump in the launch table. LifeSciBench posted the biggest relative leap of all, climbing from 50.4% to 59.9%, a 9.5-point gain in one release cycle. None of these are marginal, and all of them point the same direction: GPT-5.6 is a broad, across-the-board upgrade rather than a narrow, benchmark-chasing patch.
DeepSeek’s generational story is more dramatic in percentage terms because it is starting from a lower base on several benchmarks. DeepSeek’s own comparison shows the base V4 model clearing 80% on SWE-bench Verified, up from roughly 49% for DeepSeek V3, a jump of more than 30 points in a single generation. The newer V4 Pro 0813 build pushes that further to 96.40%, according to Vals.ai, meaning the DeepSeek family has gone from roughly half of issues resolved to nearly all of them across two release cycles. The Vals Index overall score tells a similar story at a broader level: V4 Pro 0813’s 52.37% is 9.48 points above the prior V4 baseline’s 42.89%, and two specific categories, ProofBench and Legal Research Bench, nearly quintupled and roughly doubled respectively.
What this means practically is that anyone benchmarking against last year’s models, or even last quarter’s, is working with stale numbers. A team that evaluated DeepSeek V4 in the spring and concluded it was not competitive on coding accuracy is looking at a model that has since gained roughly 16 points on SWE-bench Verified in the Pro 0813 refresh. A team that dismissed GPT-5.5 as too expensive for BrowseComp-style research tasks should re-run that math against GPT-5.6 Terra or Luna, both of which beat GPT-5.5’s BrowseComp score while costing a fraction of GPT-5.5’s per-token price. Model comparisons age fast in this market, and August 2026’s numbers are already meaningfully different from what either lab published in the spring.
Developer Ecosystem and Access Options
Access patterns differ almost as much as pricing does. GPT-5.6 is reachable exactly one way: OpenAI’s API, or products built on top of it like ChatGPT’s enterprise tiers. There is no alternate host, no community fork, and no way to run it outside OpenAI’s infrastructure. That simplicity has an upside, since every GPT-5.6 deployment behaves identically no matter who is calling it, with the same rate limits and the same guarantees.
DeepSeek V4 Pro 0813 has a noticeably wider surface area. Beyond DeepSeek’s own API, the model is listed on OpenRouter for teams that want a single billing relationship across multiple model providers, where as of August 2026 OpenRouter prices cache-read tokens at $0.05808 per million, a steep discount off its $0.5808 standard input rate, and open-weight releases in this family have historically shown up on Hugging Face for teams that want to download and inspect the weights directly before deploying. Search interest around terms like “deepseek v4 pro huggingface,” “deepseek v4 pro ollama,” and “deepseek v4 pro openrouter” reflects real demand from developers trying to run the model outside DeepSeek’s own hosted API, whether for cost control, latency, or compliance reasons. That ecosystem breadth is a direct consequence of the open-weight decision covered earlier in this article, and it is one of the more underrated practical differences between the two models once you move past benchmark tables.
Pricing and Cost Per Task Compared
Price is where the two models genuinely live in different universes, and it is worth walking through the full table rather than reducing it to a single headline multiple, because both models have tiered structures that change the real answer depending on your workload.
| Model / Tier | Input per 1M tokens | Output per 1M tokens | Cached input per 1M | Source |
|---|---|---|---|---|
| GPT-5.6 Sol | $5.00 | $30.00 | $0.50 | OpenAI API pricing |
| GPT-5.6 Terra | $2.00 | $12.00 | $0.20 | OpenAI, post July 30 cut |
| GPT-5.6 Luna | $0.20 | $1.20 | $0.02 | OpenAI, post July 30 cut |
| DeepSeek V4 Pro 0813 (cache hit, off-peak) | $0.022 | $1.98 | n/a | DeepSeek API docs |
| DeepSeek V4 Pro 0813 (cache miss, off-peak) | $0.66 | $1.98 | n/a | DeepSeek API docs |
| DeepSeek V4 Pro 0813 (cache miss, peak) | $1.32 | $3.96 | n/a | DeepSeek API docs |
| DeepSeek V4 Pro 0813 via OpenRouter | $0.66 | $1.98 | $0.022 | OpenRouter listing |
DeepSeek runs a peak and off-peak pricing model that took effect August 16, 2026 at 16:00 UTC, with peak hours defined as 01:00 to 04:00 UTC and 06:00 to 10:00 UTC, according to DeepSeek’s own API docs and a launch analysis from Enterprise DNA. That structure is the latest step in a pricing path that has moved fast all year: V4-Pro launched in April 2026 at $1.74 per million input tokens and $3.48 per million output tokens, with cache-hit input priced at $0.145 per million, before a 75% promotional discount cut those rates to $0.435 input and $0.87 output through May 31, 2026, and DeepSeek then made that discount permanent that same day, fixing pricing at $0.435 input, $0.87 output, and $0.003625 per million on cache-hit tokens. Reuters independently confirmed the general-availability peak rate for the newer V4 Pro 0813 build at $1.32 per million input tokens and $3.96 per million output tokens, and DeepSeek’s docs put off-peak rates at exactly half of peak rates across the board. That is a genuinely unusual structure for an API and means the real cost of a DeepSeek V4 Pro 0813 workload depends on when in the day you run it, something GPT-5.6’s flat pricing never asks you to think about.
Even at DeepSeek’s OpenRouter-hosted rate, which OpenRouter lists as $0.5808 per million input tokens and $1.742 per million output tokens for V4 Pro 0813 as of August 2026, GPT-5.6 Sol’s $5.00 input and $30.00 output is still roughly 8.6 times more expensive on input and about 17 times more expensive on output. Compare against DeepSeek’s cheapest official off-peak cache-miss tier, $0.66 input, and the input gap narrows slightly to about 7.5 times. Compare against the deepest cache-hit discount, $0.022, and you get the 227 times figure that opens this article. Which number is “real” depends entirely on your cache hit rate, your hosting provider, and what time of day your workload runs, which is exactly why teams evaluating a migration need to model their own traffic pattern rather than trust a single multiple.
On the GPT-5.6 side, cost separates cleanly by tier instead of by time of day. Artificial Analysis reports GPT-5.6 Sol running at max reasoning effort costs about $1.04 per task in its Intelligence Index benchmark, roughly one-third the cost of Claude Fable 5 for a comparable intelligence score. Artificial Analysis has run the same blended-cost math on the DeepSeek side too, reporting V4-Pro’s effective price at roughly $0.18 per million tokens once you weight input, output, and cache-hit traffic in a realistic 7:2:1 mix, a figure it published back in May 2026 that still frames just how far apart the two pricing philosophies sit. That framing matters: OpenAI is not positioning Sol as cheap in absolute terms, it is positioning it as cheap relative to other closed-weight frontier labs, which is a different pitch than DeepSeek’s open-weight, fractions-of-a-cent pitch.
Context Window, Speed and Latency
Both models land in roughly the same context window class. GPT-5.6’s three tiers all carry approximately 1.05 million tokens of context, according to OpenAI’s model documentation pages. DeepSeek V4 Pro 0813’s context window is confirmed at a 1,000,000-token limit on DeepSeek’s own model page, per an August 2026 write-up from Unite.AI, which also lists a maximum output ceiling of 384,000 tokens for the model. That same 1,000,000-token figure was already locked in back when DeepSeek first confirmed it on Hugging Face in April 2026, so the 0813 refresh carried the context window forward unchanged rather than expanding it. It is worth noting that some older DeepSeek pricing guides still reference a smaller 256K window for a prior V4 Pro release, a discrepancy that likely reflects the difference between the original V4 Pro and the refreshed 0813 build rather than a live conflict.
Neither company has published a directly comparable, apples-to-apples latency benchmark covering both models in the same test run. What is published separately: GPT-5.6 Sol offers a “Fast” surface at $10.00 input and $60.00 output per million tokens, roughly double the standard rate, specifically for workloads that need lower latency at any cost, according to a pricing breakdown from Digital Applied. DeepSeek has not published an equivalent low-latency premium surface for V4 Pro 0813 in the sources reviewed here, since its peak/off-peak model is about server load and cost rather than a latency guarantee.
For teams building latency-sensitive agent loops, that asymmetry matters more than raw benchmark scores. GPT-5.6 gives you a paid lever to pull for speed. DeepSeek V4 Pro 0813 gives you a cost lever tied to the clock instead, which rewards scheduling flexibility rather than paying extra for guaranteed speed.
Open-Weight vs Closed-Weight: What It Actually Means
The single biggest structural difference between these two models has nothing to do with any benchmark table. GPT-5.6 is API-only. You cannot download the weights, you cannot run it on your own hardware, and every request goes through OpenAI’s infrastructure and its rate limits. DeepSeek V4 Pro 0813 ships as open weights, which means a team with enough GPU capacity can self-host the model entirely, eliminating per-token API costs after the hardware investment and keeping every request on infrastructure they control.
That openness comes with real tradeoffs. Self-hosting a model at the scale DeepSeek’s V4 Pro family runs at requires serious GPU infrastructure, ongoing ops work, and someone on the team who actually understands inference serving. For most companies, running DeepSeek V4 Pro 0813 through a hosted provider like OpenRouter or DeepSeek’s own API is the practical path, which puts you back in per-token pricing territory, just at DeepSeek’s rates instead of OpenAI’s.
Where open weights matter most is regulatory and data-residency scenarios. A bank, hospital system, or government contractor that legally cannot send data to a third-party API at all has no path to using GPT-5.6, full stop. That same organization can self-host DeepSeek V4 Pro 0813 inside its own data center and never send a token outside its network boundary. That single structural fact drives a meaningful share of DeepSeek’s enterprise adoption regardless of what any individual benchmark shows.
Five Real-World Use Cases Where Each Model Wins
1. Regulated-industry internal tooling. A healthcare or financial services team building an internal coding assistant that can never touch a third-party API is a clean fit for DeepSeek V4 Pro 0813 self-hosted on internal GPUs, sidestepping the data-residency problem entirely while still getting a 96.40% SWE-bench Verified score, the highest of any open-weight model tracked by Vals.ai.
2. High-volume terminal-driven CI agents. A team running agents that chain dozens of shell commands per task, debug live test failures, and need to recover gracefully from broken states should lean toward GPT-5.6 Sol or Terra, given the 87-89% range on Terminal-Bench 2.1 versus DeepSeek V4 Pro 0813’s 54.68% on the same test.
3. Cost-sensitive bulk code review. A startup running automated code review across thousands of pull requests a month, where each individual review does not need frontier-level reasoning, is better served by DeepSeek V4 Pro 0813’s off-peak cache-hit pricing or GPT-5.6 Luna’s $0.20 input rate, both of which cut per-task cost by orders of magnitude versus running everything through Sol.
4. Research assistants that browse the live web. A product that needs to search, read, and synthesize current information, rather than rely purely on training data, benefits from GPT-5.6 Sol’s 90.4% BrowseComp score, a benchmark where DeepSeek V4 Pro 0813 has no directly published equivalent in the sources reviewed.
5. Legal document analysis and contract review. A legal-tech tool parsing contracts and case law is a strong match for DeepSeek V4 Pro 0813, whose Legal Research Bench score jumped from 23.08% to 40.87% in this release, moving it from rank #27 to #11 on the Vals Index, one of the largest single-release gains either company published this cycle.
Migration Guide: Moving Between GPT-5.6 and DeepSeek V4 Pro 0813
Both providers expose OpenAI-compatible chat completion endpoints, so most migrations are a configuration change rather than a rewrite. Here is a practical path for teams moving workloads from GPT-5.6 to DeepSeek V4 Pro 0813, or the reverse.
- Audit your current prompt library for anything relying on GPT-5.6-specific features, like the Fast surface’s latency guarantee, that DeepSeek does not offer an equivalent for.
- Benchmark a representative sample of your own production tasks against both models, not just the public benchmarks in this article, since real-world prompt distributions rarely match SWE-bench or Terminal-Bench exactly.
- If your workload is terminal-heavy or browsing-heavy, keep it on GPT-5.6 Sol or Terra given the Terminal-Bench and BrowseComp gaps documented above.
- If your workload is bulk code review, legal document parsing, or anything where a 96.40% SWE-bench Verified score matters more than agentic tool orchestration, pilot DeepSeek V4 Pro 0813.
- Swap your API base URL and model identifier. DeepSeek’s OpenAI-compatible endpoint accepts standard chat completion payloads, so most SDK code needs only a base URL and API key change.
from openai import OpenAI
# Pointed at DeepSeek's OpenAI-compatible endpoint instead of OpenAI's
client = OpenAI(
base_url="https://api.deepseek.com/v1",
api_key="YOUR_DEEPSEEK_API_KEY",
)
response = client.chat.completions.create(
model="deepseek-v4-pro-0813",
messages=[
{"role": "user", "content": "Refactor this function for readability."}
],
)
print(response.choices[0].message.content)- Schedule cost-sensitive batch jobs, like nightly code review sweeps, inside DeepSeek’s off-peak window (outside 01:00-04:00 and 06:00-10:00 UTC) to lock in the lower half of its peak/off-peak pricing.
- Monitor cache hit rate closely if you move to DeepSeek. The gap between its $0.007 cache-hit and $0.22 cache-miss off-peak input pricing is over 31 times, so prompt structure that maximizes cache reuse has an outsized effect on your actual bill.
- Keep a fallback path to the other model for the specific task types where the benchmark data in this article shows a clear gap, rather than committing 100% of traffic to either model on day one.
Pros and Cons
GPT-5.6 Sol/Terra/Luna
Pros: Leads DeepSeek V4 Pro 0813 decisively on Terminal-Bench 2.1 (88.8% vs 54.68%). Strong BrowseComp score (90.4%) for live web research tasks. Three price/performance tiers let you match cost to task complexity without switching models. Flat, predictable pricing with no time-of-day variance. A paid “Fast” surface exists for latency-critical workloads.
Cons: Closed weights mean no self-hosting option, ever, regardless of your compliance needs. Sol’s $5.00/$30.00 per-million-token pricing is dramatically more expensive than DeepSeek’s cheapest tiers. Trails several Anthropic models on SWE-Bench Pro at 64.6%, per GoML’s analysis. No published SWE-bench Verified score directly comparable to DeepSeek’s 96.40%.
DeepSeek V4 Pro 0813
Pros: Highest SWE-bench Verified score of any open-weight model tracked by Vals.ai, at 96.40%. Open weights enable self-hosting for regulated industries with hard data-residency requirements. Off-peak cache-hit pricing as low as $0.007 per million input tokens. Large single-release jumps on ProofBench and Legal Research Bench.
Cons: Weak on Terminal-Bench 2.1 relative to GPT-5.6, at 54.68% versus Sol’s 88.8%. Peak/off-peak pricing adds scheduling complexity flat-rate competitors do not have. No published BrowseComp or Artificial Analysis Coding Agent Index score for direct comparison. Self-hosting the full model requires infrastructure most teams do not have in-house.
Which Model Should You Choose?
Choose GPT-5.6 Sol if your workload is agentic and terminal-driven, meaning your model needs to chain shell commands, recover from errors mid-task, and operate a CLI reliably, since the 88.8% Terminal-Bench 2.1 score is the clearest differentiator in this entire comparison.
Choose GPT-5.6 Terra or Luna if you want GPT-5.6’s agentic strengths but Sol’s pricing does not fit your volume, since Terra ($2.00/$12.00) and Luna ($0.20/$1.20) both improved on GPT-5.5’s SWE-Bench Pro score while costing a fraction of Sol.
Choose DeepSeek V4 Pro 0813 hosted via API if your workload is bug-fix and small-feature coding at high volume, where the 96.40% SWE-bench Verified score and off-peak cache-hit pricing combine to make it dramatically cheaper per equivalent task than any GPT-5.6 tier.
Choose DeepSeek V4 Pro 0813 self-hosted if you operate in a regulated industry where sending data to any third-party API is a non-starter, since open weights are the only structural way to solve that problem with either model in this comparison.
Choose a hybrid setup, routing terminal-heavy and browsing-heavy tasks to GPT-5.6 and bulk coding or legal-document tasks to DeepSeek V4 Pro 0813, if your product spans multiple task types, since the benchmark gaps in each direction are large enough that no single model wins everything.
The Verdict: GPT-5.6 vs DeepSeek V4 Pro 0813
Neither model is a strict upgrade over the other, and the data backs that up cleanly. GPT-5.6 Sol wins agentic, terminal-driven, and browsing tasks by wide margins: 88.8% versus 54.68% on Terminal-Bench 2.1, 90.4% on BrowseComp with no directly comparable DeepSeek number to beat it. DeepSeek V4 Pro 0813 wins on raw coding accuracy for the older, more saturated SWE-bench Verified test, at 96.40%, the best open-weight score tracked by Vals.ai, and it wins on price by a factor that ranges from roughly 7.5 times to over 700 times depending on which tier and cache scenario you compare.
For a team building an autonomous coding agent that lives in a terminal, GPT-5.6 remains the safer default despite the cost. For a team running high-volume, well-defined coding or document-analysis tasks where cost per task matters more than agentic sophistication, DeepSeek V4 Pro 0813 is very hard to argue against, especially once you factor in the option to self-host. The honest answer, based on every published number reviewed for this comparison, is that most serious engineering teams will end up running both, routed by task type, rather than standardizing on either one alone.
Frequently Asked Questions
Is DeepSeek V4 Pro 0813 actually open source?
It is open-weight, meaning the model weights are downloadable and self-hostable, which is different from full open-source in the traditional sense of published training code and data. That distinction matters for compliance teams evaluating it, but for most practical purposes, “open-weight” is what unlocks the self-hosting and data-residency benefits discussed in this article.
Why does DeepSeek have peak and off-peak pricing?
DeepSeek introduced peak/off-peak pricing for V4 Flash and V4 Pro effective August 16, 2026, at 16:00 UTC, with peak hours set at 01:00-04:00 UTC and 06:00-10:00 UTC, according to a launch analysis from Enterprise DNA. Off-peak rates run at exactly half of peak rates, which is likely a way to smooth server load across the day by giving cost-sensitive customers an incentive to shift batch workloads to quieter hours.
Which model is better for coding, GPT-5.6 or DeepSeek V4 Pro 0813?
It depends on the type of coding task. DeepSeek V4 Pro 0813 leads on SWE-bench Verified (96.40%), a benchmark for resolving discrete, well-defined GitHub issues. GPT-5.6 Sol leads on Terminal-Bench 2.1 (88.8% vs 54.68%), a benchmark for multi-step terminal automation and agentic coding workflows. Neither is a better “coding model” across the board.
How much cheaper is DeepSeek V4 Pro 0813 than GPT-5.6 Sol?
At DeepSeek’s off-peak cache-hit input rate of $0.007 per million tokens versus GPT-5.6 Sol’s $5.00, the gap is about 714 times. At DeepSeek’s more typical off-peak cache-miss rate of $0.22, the gap narrows to about 23 times. At DeepSeek’s OpenRouter-hosted rate of $0.66, it is about 7.5 times cheaper on input. The real number for your workload depends heavily on your cache hit rate and time of day.
Can I self-host GPT-5.6?
No. GPT-5.6 is closed-weight and API-only through OpenAI. There is no path to running it on your own infrastructure, which is a structural limitation for any organization with hard data-residency or compliance requirements that prohibit sending data to third-party APIs.
What is the context window for each model?
GPT-5.6’s three tiers, Sol, Terra and Luna, all carry approximately 1.05 million tokens of context, per OpenAI’s model documentation. DeepSeek V4 Pro 0813 is listed at 1.0 million tokens in the most recent cost calculator tied to the 0813 build, putting the two models in roughly the same context class.
Does GPT-5.6 Sol beat every Claude model on coding benchmarks?
No. Analysis from GoML notes that on SWE-Bench Pro, GPT-5.6 Sol’s 64.6% still trails Claude Opus 4.8, Claude Fable 5, and Claude Mythos 5 on the same benchmark. GPT-5.6 does lead the Artificial Analysis Coding Agent Index v1.1 at max reasoning, scoring 80, which Artificial Analysis reports as 2.8 points above Claude Fable 5 on that specific composite index.
Should I run GPT-5.6 and DeepSeek V4 Pro 0813 together?
For most teams building products that span multiple task types, yes. The benchmark gaps documented in this article run in both directions, GPT-5.6 wins terminal automation and web research, DeepSeek V4 Pro 0813 wins bulk coding accuracy and price, so routing requests by task type rather than standardizing on one model captures the strengths of both.
Has DeepSeek V4 Pro 0813 replaced the older DeepSeek V4 Pro entirely?
V4 Pro 0813 is the current build developers should default to, since it is the version reflected in DeepSeek’s own official pricing docs and in the latest Vals.ai and BenchLM leaderboard entries as of August 19-20, 2026. Some older third-party pricing guides still reference the prior V4 Pro release’s numbers, including a smaller 256K context window and different token pricing, so double-check that any guide or wrapper you use is dated after August 13, 2026 before trusting its figures. That window is closing fast, though: DeepSeek’s own API changelog notes that after 12:00 Beijing time on September 14, 2026, any call using the `deepseek-v4-pro` model identifier gets automatically routed to V4.1 Flash instead, so teams pinning that exact model string should update their integration before that cutover rather than after.
