Claude Sonnet 5 vs DeepSeek V4: Closed Frontier vs Open-Weight Price (2026)

Claude Sonnet 5 and DeepSeek V4 split the decision rather than crown one winner. On the single benchmark both vendors report the same way, SWE-bench Pro, Claude Sonnet 5 posts 63.2 percent against DeepSeek V4-Pro's 55.4 percent, and Sonnet 5 uniquely documents computer use plus vision. DeepSeek V4 answers with open MIT-licensed weights you can self-host and pricing that runs roughly four to five times cheaper on input and about eleven times cheaper on output than Sonnet 5's introductory rate, so the right pick turns on whether documented capability and governance matter more than raw token cost and openness.

We compared Claude Sonnet 5 and DeepSeek V4 as two very different answers to the same question: what does a strong, long-context coding-and-reasoning model look like in mid-2026? One is a closed frontier release from Anthropic with a public system card. The other is an open-weight release from DeepSeek that you can download and run yourself. Every benchmark number in this comparison is vendor-reported, taken from each company's own system card or model card, and has not been independently reproduced by us. We say so up front because the honest version of this matchup depends entirely on reading those figures carefully rather than stacking them into a false ranking.

Quick Verdict

There is no overall winner here, and that is a deliberate call rather than a hedge. Claude Sonnet 5 wins the one clean shared benchmark (SWE-bench Pro, 63.2 out of a possible 100 versus 55.4), is the only one of the two to publish a computer-use score (OSWorld-Verified 81.2), handles vision as well as text, and ships inside Anthropic's mature ecosystem with a public safety system card. DeepSeek V4 wins price decisively, ships open weights under an MIT license you can self-host, matches Sonnet's one-million-token context window, and posts a strong self-reported reasoning battery.

Pick Claude Sonnet 5 if you need documented computer use, vision and multimodal input, a governance or compliance sign-off that a public system card supports, or the Claude Code and Claude.ai ecosystem. Pick DeepSeek V4 if token cost, open weights, self-hosting, or avoiding vendor lock-in dominate your decision, and you can accept a text-only model plus a China-hosted API (or you run the weights yourself). On our own editorial scale we rate Claude Sonnet 5 at 9.3 out of 10 and DeepSeek V4 at 8.7 out of 10, and the gap is almost entirely about capability breadth versus cost, not about one model being broadly better than the other.

How We Compared Them

Comparisons between models from different labs go wrong when numbers that look similar get placed side by side even though they measure different things. We wrote three rules for this piece and kept to them.

First, only same-test, same-scale numbers appear next to each other. The one place where Claude Sonnet 5 and DeepSeek V4 can be lined up honestly is SWE-bench Pro, because both vendors publish a score on that exact test. Everything else is shown single-sided, clearly labeled as belonging to only one model, because no matching figure exists from the other vendor.

Second, we do not manufacture a counterpart when one is missing. DeepSeek publishes LiveCodeBench, Codeforces, GPQA Diamond and MMLU-Pro results; Claude Sonnet 5's published sheet does not give same-test counterparts for those, so we present DeepSeek's as its own reported figures and decline to pair them. Likewise, Claude Sonnet 5's OSWorld-Verified computer-use score has no DeepSeek equivalent, so it stands alone.

Third, all figures are vendor-reported and not independently reproduced by us, and cross-vendor pricing is approximate because Claude and DeepSeek tokenize text differently. A price quoted per million tokens is not a perfectly clean per-word comparison when two models split the same paragraph into a different number of tokens. We flag the direction and the rough magnitude of the price gap, not a decimal-precise ratio. With those rules stated, here is how each model looks.

Meet Claude Sonnet 5

Claude Sonnet 5 is Anthropic's mid-tier model, released on June 30, 2026, sitting below the flagship Opus 4.8 and replacing the previous Sonnet 4.6. It is a closed model, available through the Claude API, inside Claude Code, and as the default model on both free and Pro tiers of Claude.ai. In our reading of the system card, Anthropic positions it as a workhorse: strong enough for serious coding and agentic work, cheap enough at its introductory price to run at scale, and safety-hardened with cyber safeguards on by default and lower reported hallucination and sycophancy than its predecessor.

On capability, the headline numbers Anthropic reports are SWE-bench Pro at 63.2 percent and OSWorld-Verified at 81.2 percent, the latter being a computer-use benchmark that measures how well the model can operate a graphical desktop environment. It carries a one-million-token context window and is multimodal, accepting image input alongside text, consistent with the rest of the Claude family. The publisher is Anthropic, and the model ships with a public system card that documents its safety posture. For teams that need to show a reviewer or a compliance function what a model was tested against, that public documentation is itself a feature.

Meet DeepSeek V4

DeepSeek V4 arrived earlier, on April 24, 2026, as the official DeepSeek V4 Preview from the publisher DeepSeek. Its defining trait is openness: the weights are released under an MIT license, published on Hugging Face, and self-hostable through inference stacks such as vLLM and SGLang. That means you can run it inside your own infrastructure without sending a single token to a hosted API, which reshapes both the cost and the data-residency conversation. The trade-off is that DeepSeek V4 is text-only; it does not accept image input the way Claude Sonnet 5 does.

The model ships in two mixture-of-experts variants. V4-Pro is the larger one at 1.6 trillion total parameters with 49 billion active per token, while V4-Flash is the lighter option at 284 billion total parameters with 13 billion active. Both share a one-million-token context window and a 384,000-token maximum output. Architecturally, DeepSeek V4 uses a hybrid attention scheme that combines token compression with what DeepSeek calls Sparse Attention, and it exposes reasoning-effort modes labeled Non-Think, Think High and Think Max so you can trade latency and cost against depth of reasoning. Notably, it runs day-one on Huawei Ascend chips, which matters for buyers building outside the Nvidia supply chain. DeepSeek's reported benchmarks, cited throughout this piece, come from the V4-Pro variant running in its maximum reasoning mode.

The mixture-of-experts design is worth understanding because it shapes both cost and behavior. Only a fraction of each variant's parameters activate per token, 49 billion of V4-Pro's 1.6 trillion and 13 billion of V4-Flash's 284 billion, which is a large part of why DeepSeek can price the model as aggressively as it does. The three reasoning-effort modes give you a further lever: Non-Think keeps latency and token spend low for straightforward requests, while Think High and Think Max spend more tokens on internal reasoning for hard problems. In practice that means the benchmark scores we cite, which come from the most expensive Think Max setting, represent a ceiling rather than what you would see at a cheaper default. It is a reminder to read any single headline number alongside the mode that produced it.

Head-to-head at a glance

The table below mirrors the nine dimensions where a direct comparison is meaningful. Note what is deliberately absent: DeepSeek's SWE-bench Verified score does not appear here, because it is a different and easier test than SWE-bench Pro and would create a misleading row. The only coding number in this table is the shared SWE-bench Pro figure.

Benchmarks: what's comparable and what isn't

This is the section that most head-to-heads get wrong, so we are going to be pedantic about it. There is exactly one benchmark you can read as a genuine head-to-head, and a longer list of numbers that only one vendor publishes.

The one shared benchmark: SWE-bench Pro

SWE-bench Pro is a demanding software-engineering benchmark, and it is the single test where both vendors report a score. Claude Sonnet 5 reports 63.2 percent. DeepSeek V4-Pro reports 55.4 percent, running in its maximum reasoning mode. That is a gap of 7.8 points in Sonnet 5's favor on the same test at the same scale. Both figures are vendor-reported and neither has been independently reproduced by us, and it is worth remembering that DeepSeek's number reflects its most expensive, highest-latency reasoning setting rather than a default configuration. Even with those caveats, this is the fair coding comparison, and on it Claude Sonnet 5 is ahead.

Sonnet-only figure: computer use

Claude Sonnet 5 reports OSWorld-Verified at 81.2 percent. OSWorld is a benchmark for computer use, meaning the model's ability to operate a real desktop interface to complete tasks. DeepSeek publishes no OSWorld score, so there is nothing to compare it against, and we specifically do not equate it with DeepSeek's Terminal-Bench 2.0 result of 67.9, because operating a graphical desktop and driving a terminal are different tasks measured by different harnesses. If documented computer-use capability is on your requirements list, Sonnet 5 is the only one of the two that answers it with a published number.

DeepSeek-only figures: a strong self-reported battery

DeepSeek publishes a wide battery of results for V4-Pro that Claude Sonnet 5's published sheet gives no same-test counterparts for, so we present them as DeepSeek's own reported figures and do not pair them with anything from Sonnet 5. In coding and reasoning, DeepSeek reports LiveCodeBench at 93.5, a Codeforces rating of 3,206 Elo, GPQA Diamond at 90.1, and MMLU-Pro at 87.5. It also reports SWE Multilingual at 76.2, Terminal-Bench 2.0 at 67.9, Humanity's Last Exam at 37.7 (rising to 48.2 with tools), MRCR at one million tokens of 83.5, BrowseComp at 83.4, MCPAtlas at 73.6 and Toolathlon at 51.8. These are impressive numbers on their face, but they are single-sided: without a matching Sonnet 5 figure on the identical test, they tell you what DeepSeek reports about itself, not who wins.

Beyond the coding suite, DeepSeek reports a set of long-context and agentic figures for V4-Pro that also stand on their own. It cites MRCR at one million tokens of 83.5, a long-context retrieval measure that suits the model's million-token window, along with BrowseComp at 83.4 for web-browsing tasks, MCPAtlas at 73.6 and Toolathlon at 51.8 for tool-use and orchestration workflows. These paint a picture of a model DeepSeek positions as capable across retrieval, browsing and tool use, not only raw code generation. As with the reasoning battery, none of them has a same-test counterpart on Claude Sonnet 5's published sheet, so we present them as DeepSeek's own claims about DeepSeek and resist the temptation to read them as a scoreboard against Sonnet 5.

The trap to avoid: SWE-bench Verified is not SWE-bench Pro

DeepSeek also reports SWE-bench Verified at 80.6, and this number deserves a warning label because it is the easiest way to accidentally mislead yourself. SWE-bench Verified is a different, easier test than SWE-bench Pro. The proof is in DeepSeek's own numbers: the exact same V4-Pro model scores 80.6 on Verified but only 55.4 on Pro, a 25-point gap between two benchmarks that share a family name. Placing DeepSeek's Verified 80.6 next to Claude Sonnet 5's Pro 63.2 would suggest DeepSeek wins coding by a wide margin, when in fact the honest same-test comparison (Pro versus Pro) has Sonnet 5 ahead. We show SWE-bench Verified 80.6 only as a DeepSeek single-sided figure, and never as a rival to Sonnet's Pro score. Whenever you see a chart that pits a Verified number against a Pro number, treat it as a red flag.

Pricing

Price is where the two models diverge most sharply, and it is DeepSeek V4's strongest argument. Claude Sonnet 5 launched with an introductory rate of two dollars per million input tokens and ten dollars per million output tokens, confirmed on Anthropic's platform and running through August 31, 2026. From September 1, 2026, the standard rate rises to three dollars per million input and fifteen dollars per million output. Cache reads are cheaper still, at twenty cents per million tokens during the introductory window, rising to thirty cents at the standard rate.

DeepSeek V4 undercuts that comfortably, and its live rates as of July 2026 carry no promotional expiry. V4-Pro is priced at roughly forty-three and a half cents per million input tokens on a cache miss, about a third of a cent per million on a cache hit, and eighty-seven cents per million output tokens. The lighter V4-Flash is cheaper again, at fourteen cents per million input, a fraction of a cent cached, and twenty-eight cents per million output.

Put those next to Sonnet 5's introductory rate and the gap is large. On input, V4-Pro runs roughly four to five times cheaper than Sonnet 5, and V4-Flash is more than an order of magnitude cheaper. On output, where the difference matters most for generation-heavy workloads, V4-Pro is about eleven times cheaper than Sonnet 5's introductory ten dollars, and V4-Flash is cheaper still. When Sonnet 5's introductory pricing ends and the standard rate takes effect, that gap widens further in DeepSeek's favor.

A simple worked example makes the scale concrete. Imagine a workload that generates one hundred million output tokens in a billing period, a realistic figure for a busy production pipeline. At Sonnet 5's introductory output rate, that is about one thousand dollars. The same one hundred million output tokens cost about eighty-seven dollars on DeepSeek V4-Pro and about twenty-eight dollars on V4-Flash. Add one hundred million input tokens and Sonnet 5 adds roughly two hundred dollars, while V4-Pro adds about forty-three dollars and V4-Flash about fourteen. The point is not the exact totals, which depend on your traffic mix, but the shape: at volume, the difference between these models is measured in multiples, not percentages, and that is before DeepSeek's cache-hit discount enters the picture.

DeepSeek's cache-hit economics are worth calling out separately. At about a third of a cent per million tokens on cached input for V4-Pro, workloads that reuse large, stable prompts, such as long system prompts, retrieval contexts, or fixed instruction sets, can drive the effective input cost close to negligible. That is a meaningful lever for high-volume, repetitive pipelines. One honest caveat applies to all of these comparisons: Claude and DeepSeek tokenize text differently, so a price quoted per million tokens is not a perfectly clean per-word ratio. The direction is unambiguous and the magnitude is large, but treat the exact multiples as approximate rather than exact.

Where each one pulls ahead

Openness and self-hosting. DeepSeek V4 wins this outright. MIT-licensed weights on Hugging Face mean you can run the model on your own hardware through vLLM or SGLang, fine-tune it, and keep every token inside your own perimeter. Claude Sonnet 5 is a managed API only; there is no self-hosted option. For organizations with strict data-residency requirements or a desire to avoid vendor lock-in, this is often the deciding factor before any benchmark is discussed.

Context window. This is a genuine tie. Both models offer a one-million-token context window, and DeepSeek V4 additionally documents a 384,000-token maximum output. Long-document and large-codebase workflows are well served by either.

Computer use. Claude Sonnet 5 pulls ahead here because it publishes an OSWorld-Verified score of 81.2 and DeepSeek publishes nothing comparable. If your use case involves an agent operating a desktop or browser interface, Sonnet 5 is the model with documented evidence behind it.

Safety and governance. Claude Sonnet 5 ships with a public system card describing lower hallucination and sycophancy than its predecessor and cyber safeguards enabled by default. For teams that need to hand a reviewer a document explaining what a model was evaluated against, that public artifact is a real advantage, independent of raw capability.

Modality. Claude Sonnet 5 handles vision as well as text; DeepSeek V4 is text-only. Any workflow that includes screenshots, diagrams, documents-as-images, or other visual input needs Sonnet 5, because DeepSeek V4 cannot accept those inputs at all.

Ecosystem and distribution. Claude Sonnet 5 benefits from Claude Code, mature SDKs, and its position as the default model on both free and Pro Claude.ai. DeepSeek V4 is distributed through its own China-hosted API, Hugging Face weights, and Huawei Ascend support. Each ecosystem is coherent, but they serve different buyers: one leans toward Western developer tooling and governance, the other toward open deployment and hardware flexibility.

Claude Sonnet 5 — pros and cons

• Pro: Wins the one shared benchmark, SWE-bench Pro, at 63.2 percent versus 55.4 percent.
• Pro: Only model of the two with a documented computer-use score (OSWorld-Verified 81.2).
• Pro: Multimodal, accepting image input alongside text.
• Pro: Public safety system card supports governance and compliance sign-off.
• Pro: Mature ecosystem: Claude Code, Claude.ai default model, established SDKs.
• Con: Materially more expensive per token, and the introductory rate rises after August 31, 2026.
• Con: Closed and managed-API only, with no self-hosting and no weights to inspect.
• Con: No open license, so no fine-tuning of the underlying model on your own infrastructure.

DeepSeek V4 — pros and cons

• Pro: Dramatically cheaper per token, roughly four to five times cheaper on input and about eleven times cheaper on output than Sonnet 5's introductory rate, with V4-Flash cheaper again.
• Pro: Open weights under an MIT license, published on Hugging Face and self-hostable via vLLM or SGLang.
• Pro: Matches Sonnet 5's one-million-token context window and documents a 384,000-token maximum output.
• Pro: Strong self-reported reasoning battery and near-negligible cached-input pricing for repetitive workloads.
• Pro: Runs day-one on Huawei Ascend chips, useful outside the Nvidia supply chain.
• Con: Text-only, with no vision or image input.
• Con: No published computer-use benchmark.
• Con: Hosted API runs in China, raising data-residency and content-moderation questions for some Western and regulated buyers (self-hosting the open weights sidesteps this).

When to pick Claude Sonnet 5 / When to pick DeepSeek V4

When to pick Claude Sonnet 5

Choose Claude Sonnet 5 when capability breadth and documentation outweigh token cost. It is the right call if your work involves computer use or desktop and browser automation, if you need to process images alongside text, or if a compliance or security function needs to see a public system card before a model is approved. It is also the natural fit if your team already lives in Claude Code or relies on Claude.ai as a default, since Sonnet 5 slots into that ecosystem with mature tooling. In short, pick Sonnet 5 when you are optimizing for what the model can do and how well its behavior is documented, and you can absorb a higher per-token price to get there.

When to pick DeepSeek V4

Choose DeepSeek V4 when cost, openness, or control are the dominant constraints. It is the right call for high-volume, generation-heavy pipelines where an eleven-times output-price advantage compounds into serious savings, for teams that want to self-host to keep data inside their own perimeter, or for anyone who wants to avoid vendor lock-in by owning the weights outright. It fits buyers deploying on Huawei Ascend hardware and those whose workloads are text-only to begin with. The conditions to accept are that you either use a China-hosted API or run the weights yourself, and that you do not need vision or a documented computer-use score. When those conditions hold, DeepSeek V4's economics are hard to argue with.

Final Verdict

There is no overall winner, and after reviewing both models' published material, we think forcing one would misrepresent the choice. Claude Sonnet 5 and DeepSeek V4 are optimized for different priorities. On the single test both vendors report the same way, SWE-bench Pro, Claude Sonnet 5 leads at 63.2 percent to 55.4 percent, and it is the only one of the two with documented computer use, vision, and a public safety system card. DeepSeek V4 counters with open MIT-licensed weights, self-hosting, a matching one-million-token context window, a strong self-reported reasoning battery, and pricing that runs several times cheaper on input and roughly eleven times cheaper on output.

So the decision is clean even though the verdict is split. Pick Claude Sonnet 5 if you need documented capability, multimodal input, or governance sign-off, and you can pay for it. Pick DeepSeek V4 if cost, open weights, self-hosting, or avoiding lock-in dominate, and a text-only model plus a China-hosted or self-hosted deployment works for you. We score Sonnet 5 at 9.3 out of 10 and DeepSeek V4 at 8.7 out of 10, and that small gap reflects capability breadth rather than one model being broadly better than the other. Match the model to your constraint, not to a leaderboard.

One last piece of practical advice: because these two models are optimized for such different priorities, the strongest teams often do not choose between them permanently. It is entirely reasonable to route governance-sensitive, vision-dependent, or computer-use tasks to Claude Sonnet 5 while sending high-volume, text-only generation to DeepSeek V4, especially if you self-host the latter. Treat this comparison as a map of where each model's strengths lie rather than a mandate to standardize on one, and revisit the decision as both vendors publish new benchmarks and adjust pricing.

Related comparisons

If you are weighing these two models against other frontier options, see how each fares elsewhere. On the Claude Sonnet 5 side, Claude Sonnet 5 vs GLM-5.2 and Claude Sonnet 5 vs Kimi K2.6 line it up against other open-weight challengers, and Claude Sonnet 5 vs Claude Opus 4.8 places it in Anthropic's own lineup. On the DeepSeek V4 side, GLM-5.2 vs DeepSeek V4, GPT-5.5 vs DeepSeek V4, and Kimi K2.7 vs DeepSeek V4 show how the open-weight model holds up against other rivals.

Frequently asked questions

Which is better for coding, Claude Sonnet 5 or DeepSeek V4?

On the one coding benchmark both vendors report the same way, SWE-bench Pro, Claude Sonnet 5 leads at 63.2 percent against DeepSeek V4-Pro's 55.4 percent, a gap of 7.8 points. Both figures are vendor-reported and not independently reproduced by us, and DeepSeek's reflects its maximum reasoning mode. So on the fair same-test comparison, Sonnet 5 is ahead, though DeepSeek closes much of the practical gap once you weigh its far lower price.

How much cheaper is DeepSeek V4 than Claude Sonnet 5?

Substantially. Against Sonnet 5's introductory rate of two dollars per million input and ten dollars per million output, DeepSeek V4-Pro runs roughly four to five times cheaper on input and about eleven times cheaper on output, at about forty-three and a half cents input and eighty-seven cents output per million tokens. The lighter V4-Flash is cheaper again, at fourteen cents input and twenty-eight cents output. The gap widens further once Sonnet 5's introductory pricing ends on August 31, 2026.

Is DeepSeek V4 open source and Claude Sonnet 5 closed?

DeepSeek V4 ships as open-weight under an MIT license, with weights on Hugging Face that you can download, self-host through vLLM or SGLang, and fine-tune. Claude Sonnet 5 is closed and available only as a managed service through the Claude API, Claude Code, and Claude.ai. If open weights and the ability to run the model yourself matter to you, that is the clearest structural difference between the two.

Why is DeepSeek's 80.6 not directly comparable to Sonnet's 63.2?

Because they are different tests. DeepSeek's 80.6 is SWE-bench Verified, while Sonnet 5's 63.2 is SWE-bench Pro, which is a harder benchmark. The proof is that the same DeepSeek V4-Pro model scores 80.6 on Verified but only 55.4 on Pro, a 25-point gap between the two. Comparing Verified against Pro would falsely suggest DeepSeek wins coding, when the honest same-test comparison (Pro versus Pro) has Sonnet 5 ahead. Always compare Verified to Verified and Pro to Pro.

What context window does each model offer?

Both offer a one-million-token context window, so this is a tie. DeepSeek V4 additionally documents a 384,000-token maximum output. For long documents or large codebases, either model has the context length to handle the workload.

Can I self-host either model?

You can self-host DeepSeek V4 but not Claude Sonnet 5. DeepSeek publishes MIT-licensed weights on Hugging Face that run through vLLM or SGLang on your own hardware, and it supports Huawei Ascend chips from day one. Claude Sonnet 5 is a managed API only, with no self-hosted deployment option.

Does either model do computer use?

Claude Sonnet 5 publishes a computer-use score, OSWorld-Verified at 81.2 percent, which measures operating a desktop interface to complete tasks. DeepSeek V4 publishes no OSWorld result, so there is no comparable figure. We do not equate DeepSeek's Terminal-Bench 2.0 score with computer use, because driving a terminal and operating a graphical desktop are different tasks. If documented computer use matters, Sonnet 5 is the one with evidence.

Are both models multimodal?

No. Claude Sonnet 5 is multimodal and accepts image input alongside text, consistent with the Claude family. DeepSeek V4 is text-only and cannot process images. If your workflow includes screenshots, diagrams, or documents as images, you need Sonnet 5.

Where is DeepSeek's API hosted, and does moderation matter?

DeepSeek's hosted API runs in China, which introduces data-residency and content-moderation considerations for some Western or regulated buyers. If that is a concern for your organization, the open weights offer a path around it: because DeepSeek V4 is self-hostable under an MIT license, you can run it inside your own infrastructure and avoid the hosted API entirely.

Should I switch to DeepSeek V4 just to save money?

Only if the savings map to your actual constraints. For high-volume, text-only, generation-heavy pipelines, DeepSeek V4's roughly eleven-times output-price advantage is a strong reason to switch. But if you rely on vision, computer use, a public safety system card for governance, or the Claude Code ecosystem, the cheaper token price does not replace those capabilities. Match the model to what your workload actually needs rather than to price alone.

Which model is safer?

Claude Sonnet 5 ships with a public system card reporting lower hallucination and sycophancy than its predecessor and cyber safeguards enabled by default, which gives governance and security teams a documented artifact to review. DeepSeek V4's openness offers a different kind of safety: you can inspect and self-host the weights, keeping data inside your own perimeter. Neither has been independently audited by us, so weigh documented behavior against deployment control based on what your risk model prioritizes.

Do the benchmark numbers here come from independent testing?

No. Every benchmark figure in this comparison is vendor-reported, taken from each company's system card or model card, and has not been independently reproduced by us. We only place numbers side by side when they come from the same test at the same scale, which is why SWE-bench Pro is the sole shared coding comparison and everything else is presented as a single-sided, clearly labeled figure.