{"id":504,"date":"2026-08-25T06:27:58","date_gmt":"2026-08-25T06:27:58","guid":{"rendered":"https:\/\/i10x.ai\/blog\/?p=504"},"modified":"2026-08-25T06:29:33","modified_gmt":"2026-08-25T06:29:33","slug":"deepseek-v4-pro-vs-claude-opus-5","status":"publish","type":"post","link":"https:\/\/i10x.ai\/blog\/deepseek-v4-pro-vs-claude-opus-5","title":{"rendered":"DeepSeek V4 Pro vs Claude Opus 5: Cost vs Flagship (2026)"},"content":{"rendered":"\n
\n\n

Comparison \u00b7 August 2026<\/p>\n\n

\nDeepSeek V4 Pro and Claude Opus 5 are a cost-versus-flagship pair, not twins. DeepSeek is a 1.6T \/ 49B activated Mixture-of-Experts model with ~1.05M text-only context and list rates near $0.53 \/ $1.05 per million tokens. Opus 5 is Anthropic\u2019s flagship for demanding reasoning, coding, and long-horizon agents, with 1M context, text\/image\/file inputs, and $5 \/ $25 list rates. This guide uses those specs, three workload costs, and live snippets. Keep both in a\nmulti-model AI workspace<\/a>\nor start on\ni10X<\/a>.\n<\/p>\n\n

\nQuick verdict<\/strong>\n

Pick DeepSeek V4 Pro if:<\/strong> the job is text-only volume, you want ~1.05M context at a fraction of Opus spend, and you will add a critic when the user might be wrong.<\/p>\n

Pick Claude Opus 5 if:<\/strong> you need files and images, a warmer complete customer email, a pedagogical bug writeup, and a refusal that teaches real lunar geology instead of mining fictional cheese.<\/p>\n

Best default for many SaaS teams:<\/strong> DeepSeek for cheap text drafts and loops. Opus 5 for multimodal, review, and trust-sensitive work. The bill gap is large enough that \u201calways Opus\u201d is a budget choice, not a quality law.<\/p>\n

Data checked: 2026-08-24 via live side-by-side API tests. Prices change. Verify live vendor pages.<\/em><\/p>\n<\/div>\n\n

\n\n\n\n\n\n\n

1,048,576<\/strong><\/p><\/td>\n

DeepSeek V4 Pro context (API)<\/p><\/td>\n<\/tr>\n

1,000,000<\/strong><\/p><\/td>\n

Claude Opus 5 context (API)<\/p><\/td>\n<\/tr>\n

$0.53 \/ $1.05<\/strong><\/p><\/td>\n

DeepSeek V4 Pro input\/output per 1M tokens (API pricing, 2026-08-24)<\/p><\/td>\n<\/tr>\n

$5 \/ $25<\/strong><\/p><\/td>\n

Claude Opus 5 input\/output per 1M tokens (API pricing, 2026-08-24)<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n\n

\n\"Bar\n
Figure 1.<\/strong> Where each model wins on relative axes (context, modalities, output cost efficiency). DeepSeek leads context slightly and cost efficiency clearly. Opus leads modalities (text, image, file). Chart: i10X.<\/figcaption>\n<\/figure>\n\n
\n\n

Persona picker<\/h2>\n\n\n\n\n\n\n\n\n

You are\u2026<\/p><\/th>\n

Start with<\/p><\/th>\n

Why<\/p><\/th>\n<\/tr>\n

Writer \/ CS \/ marketer<\/p><\/td>\n

Claude Opus 5 (often); DeepSeek for volume<\/p><\/td>\n

Opus\u2019s captured rewrite was warmer and more complete. DeepSeek was also complete, with extra greeting padding.<\/p><\/td>\n<\/tr>\n

Developer \/ agent builder<\/p><\/td>\n

Opus default for review; DeepSeek for cheap text loops<\/p><\/td>\n

Both patched the empty-list bug. Opus explained the 0\/0 path. Vendor copy puts Opus on code review and bug finding.<\/p><\/td>\n<\/tr>\n

Researcher \/ analyst<\/p><\/td>\n

Opus when PDFs and images matter<\/p><\/td>\n

DeepSeek is text only. Opus takes text, image, and file. DeepSeek\u2019s window is slightly larger (1,048,576 vs 1M).<\/p><\/td>\n<\/tr>\n

Budget \/ high volume API<\/p><\/td>\n

DeepSeek V4 Pro<\/p><\/td>\n

Chat ~$0.0011 vs $0.0175; repo ~$0.046 vs $0.50; cached agent ~$0.078 vs $1.05.<\/p><\/td>\n<\/tr>\n

Trust \/ refuse-first workflows<\/p><\/td>\n

Claude Opus 5<\/p><\/td>\n

Opus treated green-cheese mining as a folk joke and taught maria\/highlands geology. DeepSeek flagged false, then humored a sampling plan.<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n

\n\n

What we are comparing (exact versions)<\/h2>\n

Multi-model AI means using more than one large language model in one work system. This page compares two specific API models, not a vague \u201ccheap Chinese model vs Claude\u201d stereotype and not older V3 \/ Opus 4.x pages.<\/p>\n\n\n\n\n\n\n\n\n

Field<\/p><\/th>\n

DeepSeek V4 Pro<\/p><\/th>\n

Claude Opus 5<\/p><\/th>\n<\/tr>\n

Provider<\/p><\/td>\n

DeepSeek<\/p><\/td>\n

Anthropic<\/p><\/td>\n<\/tr>\n

API model<\/p><\/td>\n

DeepSeek V4 Pro<\/code><\/p><\/td>\n

Claude Opus 5<\/code><\/p><\/td>\n<\/tr>\n

Listed API name<\/p><\/td>\n

DeepSeek: DeepSeek V4 Pro 0423<\/p><\/td>\n

Claude Opus 5<\/p><\/td>\n<\/tr>\n

Family \/ tier<\/p><\/td>\n

V4 Pro MoE, 1.6T total \/ 49B activated<\/p><\/td>\n

Opus flagship; demanding reasoning, coding, long-horizon agents<\/p><\/td>\n<\/tr>\n

App vs API note<\/p><\/td>\n

Also in DeepSeek products; this article uses the API model above<\/p><\/td>\n

Also in Claude products; this article uses the API model above<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

We use the human name DeepSeek V4 Pro (listed build 0423 on the card we pulled). For routing across many models, see\nAI model routing<\/a>.<\/p>\n\n

\n\n

Spec sheet (API, 2026-08-24)<\/h2>\n\n\n\n\n\n\n\n\n\n\n

Spec<\/p><\/th>\n

DeepSeek V4 Pro<\/p><\/th>\n

Claude Opus 5<\/p><\/th>\n<\/tr>\n

Context window<\/p><\/td>\n

1,048,576 tokens<\/p><\/td>\n

1,000,000 tokens<\/p><\/td>\n<\/tr>\n

Max output (if published)<\/p><\/td>\n

Not published on the card we pulled<\/p><\/td>\n

Not published on the card we pulled<\/p><\/td>\n<\/tr>\n

Input modalities (card)<\/p><\/td>\n

text<\/p><\/td>\n

text, image, file<\/p><\/td>\n<\/tr>\n

Output<\/p><\/td>\n

text<\/p><\/td>\n

text<\/p><\/td>\n<\/tr>\n

Architecture note<\/p><\/td>\n

MoE: 1.6T total parameters, 49B activated<\/p><\/td>\n

Not stated on the card we pulled<\/p><\/td>\n<\/tr>\n

Open weights<\/p><\/td>\n

Not listed as open weights on the card we pulled<\/p><\/td>\n

Not listed as open weights on the card we pulled<\/p><\/td>\n<\/tr>\n

Vendor positioning (short)<\/p><\/td>\n

Advanced reasoning and coding at 1M context<\/p><\/td>\n

Flagship reasoning, coding, code review, bug finding, visual analysis<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

DeepSeek\u2019s window is slightly larger. Opus takes images and files. We are not inventing SWE-bench or GPQA numbers for this pair. Live pack plus the card is the evidence.<\/p>\n\n

\n\n

Pricing and real workload cost<\/h2>\n

List prices are easy to misread. Workload cost is what you feel. Rates below are from published API pricing on 2026-08-24<\/strong>.<\/p>\n\n\n\n\n\n\n

Price<\/p><\/th>\n

DeepSeek V4 Pro<\/p><\/th>\n

Claude Opus 5<\/p><\/th>\n<\/tr>\n

Input \/ 1M tokens<\/p><\/td>\n

$0.526<\/p><\/td>\n

$5.00<\/p><\/td>\n<\/tr>\n

Output \/ 1M tokens<\/p><\/td>\n

$1.052<\/p><\/td>\n

$25.00<\/p><\/td>\n<\/tr>\n

Cache read \/ 1M<\/p><\/td>\n

$0.044<\/p><\/td>\n

$0.50<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Opus input is about 10x<\/strong> DeepSeek ($5 vs $0.53). Output is about 24x<\/strong> ($25 vs $1.05). Cache reads are about 11x ($0.50 vs $0.044).<\/p>\n\n\n\n\n\n\n

Scenario<\/p><\/th>\n

Assumed tokens<\/p><\/th>\n

Est. DeepSeek V4 Pro<\/p><\/th>\n

Est. Claude Opus 5<\/p><\/th>\n<\/tr>\n

Chat turn<\/p><\/td>\n

1k in + 0.5k out<\/p><\/td>\n

$0.0011<\/p><\/td>\n

$0.0175<\/p><\/td>\n<\/tr>\n

Repo \/ doc review<\/p><\/td>\n

80k in + 4k out<\/p><\/td>\n

$0.046<\/p><\/td>\n

$0.50<\/p><\/td>\n<\/tr>\n

Agent loop<\/p><\/td>\n

200k in (50% cached) + 20k out<\/p><\/td>\n

$0.078<\/p><\/td>\n

$1.05<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\n\"Bar\n
Figure 2.<\/strong> Estimated USD per run using published API list rates (2026-08-24). Opus is an order of magnitude more on repo review and the cached agent loop. Chart: i10X.<\/figcaption>\n<\/figure>\n

A thousand cached agent loops are about $78 on DeepSeek vs about $1,050 on Opus at these list rates. That is why routing exists. It is also why you should not send Opus-priced work to a text-only model that will humor a false world. For subscription stacks, see\nAI subscription stack cost<\/a>.<\/p>\n\n

\n\n

Performance by job (specs + live pack)<\/h2>\n

No invented benches. Method:\nside-by-side AI comparison<\/a>.<\/p>\n\n

Coding and agents<\/h3>\n

Opus 5\u2019s card stresses end-to-end software, code review, bug finding, and long-horizon agents. DeepSeek\u2019s card stresses advanced reasoning and coding at 1M context. Empty-list test: both named ZeroDivisionError and guarded with if not nums<\/code>. DeepSeek\u2019s fix is short (return 0.0). Opus walks the loop-never-runs \/ total-stays-0 \/ return-still-does-0\/0 story and mentions a caller from an empty filter. Same patch class. Opus is the better review note.<\/strong> Use DeepSeek to draft; use Opus to review when the diff matters.<\/p>\n\n

Writing and tone<\/h3>\n

DeepSeek: week-hope greeting, Q3 deck, finance delay, next week maybe Wednesday, Acme pricing on the competitive slide, long thanks. Opus: \u201cI hope you\u2019re doing well!\u201d, same operational facts, Wednesday with an offer to adjust, competitive slide started. Opus is the warmer complete Anthropic letter. DeepSeek is a usable volume draft. Neither dropped the core Q3 \/ Finance \/ meeting facts in the captured window.<\/p>\n\n

Research, math, reasoning<\/h3>\n

DeepSeek: Moon is not cheese; silicates and metals; then \u201cif we humor the hypothetical,\u201d start sampling \u201cgreen cheese.\u201d Opus: folk joke, not a fact; Apollo and Luna samples; maria basalt, highland anorthosite, dusty regolith; no cheese, no protein, no biological organic matter. Opus is the trust pick.<\/strong> DeepSeek\u2019s flag-then-collaborate pattern is the risk of using the cheap model as the last model.<\/p>\n\n

Multimodal and long context<\/h3>\n

DeepSeek 1,048,576 tokens, text only. Opus 1,000,000 tokens, text\/image\/file. For giant text pastes, DeepSeek has a small window edge. For PDFs and screenshots, Opus is the card that can see them. Visual analysis is in the Opus vendor line; we did not run a screenshot pack in this batch.<\/p>\n\n

Speed<\/h3>\n

Not measured here. Measure p50 from your region.<\/p>\n\n\n\n\n\n\n\n\n\n\n

Job<\/p><\/th>\n

Edge<\/p><\/th>\n

Why<\/p><\/th>\n<\/tr>\n

Hard coding \/ review<\/p><\/td>\n

Claude Opus 5 (this pack + vendor line)<\/p><\/td>\n

Clearer 0\/0 walkthrough; code review \/ bug-finding copy<\/p><\/td>\n<\/tr>\n

Cheap text coding loops<\/p><\/td>\n

DeepSeek V4 Pro<\/p><\/td>\n

Correct micro-fix at a fraction of the bill<\/p><\/td>\n<\/tr>\n

Everyday writing<\/p><\/td>\n

Claude Opus 5 (often)<\/p><\/td>\n

Warmer complete letter; DeepSeek fine for volume<\/p><\/td>\n<\/tr>\n

Long files \/ images<\/p><\/td>\n

Claude Opus 5<\/p><\/td>\n

File + image on the card<\/p><\/td>\n<\/tr>\n

False-premise handling<\/p><\/td>\n

Claude Opus 5<\/p><\/td>\n

Geology refuse; DeepSeek humors cheese mining<\/p><\/td>\n<\/tr>\n

Cost at volume<\/p><\/td>\n

DeepSeek V4 Pro<\/p><\/td>\n

Order-of-magnitude cheaper on repo and agent-loop estimates<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n

\nHow to read this<\/strong>\n

The point of this pair is routing, not a trophy. Paying Opus for every chat turn is how teams blow the budget. Using only DeepSeek on files and false worlds is how teams ship junk. Method:\nside-by-side AI comparison<\/a>.<\/p>\n<\/div>\n\n

\n\n

Side-by-side test (live API test, 2026-08-24)<\/h2>\n

Same prompts on DeepSeek V4 Pro<\/code> and Claude Opus 5<\/code> in a multi-model workspace. Three live prompts. Editorial 1-5 on instruction following, depth, factual caution, style, and usefulness (max 25 per prompt).<\/p>\n\n

Test 1: Client email rewrite<\/h3>\n

Task:<\/strong> Keep every fact. Warmer. Under 120 words.<\/p>\n

DeepSeek V4 Pro (excerpt):<\/strong> Week-hope greeting, Q3 follow-up, finance still missing after Friday, maybe Wednesday next week, competitive slide still needs Acme pricing, thanks plus \u201clet me know your thoughts.\u201d<\/p>\n

Claude Opus 5 (excerpt):<\/strong> \u201cI hope you\u2019re doing well!\u201d, Q3 deck from last Tuesday, finance promised Friday and nothing received, Wednesday next week with an offer to adjust, then the competitive slide.<\/p>\n

Edge:<\/strong> Opus for warmth and structure. DeepSeek for a complete cheap draft that already includes Acme pricing.<\/p>\n\n

Test 2: Empty-list average bug<\/h3>\n

Both correct. DeepSeek: short diagnosis, return 0.0<\/code> guard. Opus: headed explanation of 0\/0 after an empty loop, then 0.0 or ValueError. Tie on the patch. Opus on the review-quality writeup.<\/strong><\/p>\n\n

Test 3: False premise (Moon cheese)<\/h3>\n

DeepSeek flags false, then starts a sampling plan for cheese-like material. Opus refuses as folk joke, names Apollo\/Luna, basalt, anorthosite, regolith, no biological protein. Opus wins factual caution by a wide margin.<\/strong> Dual-check publishable claims:\nmulti-model hallucination checks<\/a>.<\/p>\n\n\n\n\n\n\n\n\n

Prompt<\/p><\/th>\n

DeepSeek V4 Pro<\/p><\/th>\n

Claude Opus 5<\/p><\/th>\n

Note<\/p><\/th>\n<\/tr>\n

Email rewrite<\/p><\/td>\n

22\/25<\/p><\/td>\n

23\/25<\/p><\/td>\n

Both complete; Opus warmer<\/p><\/td>\n<\/tr>\n

Bug fix<\/p><\/td>\n

23\/25<\/p><\/td>\n

24\/25<\/p><\/td>\n

Both correct; Opus more pedagogical<\/p><\/td>\n<\/tr>\n

False premise<\/p><\/td>\n

20\/25<\/p><\/td>\n

24\/25<\/p><\/td>\n

DeepSeek humors cheese mining after the flag<\/p><\/td>\n<\/tr>\n

Total (this pack)<\/strong><\/p><\/td>\n

65\/75<\/strong><\/p><\/td>\n

71\/75<\/strong><\/p><\/td>\n

Opus quality lead; DeepSeek cost lead<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Six editorial points is not why Opus costs ~13x on the agent loop. Modalities and refuse-first behavior are. Put DeepSeek first on text volume. Put Opus last on the steps you would defend in a review.<\/p>\n\n

\n\n

Ecosystem and where you run them<\/h2>\n