DeepSeek V4 Pro vs GPT-5.6 Sol: Cost, Modalities & Tests (2026)

Comparison · August 2026

DeepSeek V4 Pro and GPT-5.6 Sol both sit in the million-token class. That is where the resemblance ends on the API card. DeepSeek is a 1.6T total / 49B activated Mixture-of-Experts model with text in and text out. Sol is OpenAI’s GPT-5.6 flagship with file, image, and text inputs. DeepSeek’s list rates are a fraction of Sol’s. This guide uses those specs, three workload costs, and live snippets. Keep both in a multi-model AI workspace or start on i10X.

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Persona picker

You are…	Start with	Why
Writer / CS / marketer	GPT-5.6 Sol (often); DeepSeek for volume drafts	Both captured letters were complete. Sol was tighter. DeepSeek added “hope you’re having a good week” and a longer close.
Developer / agent builder	Sol if tools involve files/images; DeepSeek for cheap text loops	Both fixed the empty-list bug. Sol added a print. Vendor copy puts Sol on command-line and multi-step coding.
Researcher / analyst	Sol when PDFs and screenshots matter	DeepSeek’s card is text only. Sol takes file and image. Context windows are both ~1.05M.
Budget / high volume API	DeepSeek V4 Pro	Chat ~$0.0011 vs $0.007; repo ~$0.046 vs $0.20; cached agent ~$0.078 vs $0.42.
Trust / refuse-first workflows	GPT-5.6 Sol	DeepSeek flagged the false Moon premise, then sketched a cheese-mining plan. Sol refused and planned real resources.

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What we are comparing (exact versions)

Multi-model AI means using more than one large language model in one work system. This page compares two specific API models, not “open vs closed” branding and not older DeepSeek V3 / GPT-5.5 writeups.

Field	DeepSeek V4 Pro	GPT-5.6 Sol
Provider	DeepSeek	OpenAI
API model	DeepSeek V4 Pro	GPT-5.6 Sol
Listed API name	DeepSeek: DeepSeek V4 Pro 0423	OpenAI: GPT-5.6 Sol
Family / tier	V4 Pro MoE, 1.6T total / 49B activated, 1M context	GPT-5.6 series flagship
App vs API note	Also in DeepSeek products; this article uses the API model above	Also in ChatGPT / OpenAI API; this article uses the API model above

The listed DeepSeek build label on the card we pulled is V4 Pro 0423. We use the human name DeepSeek V4 Pro in the rest of this page. For routing across many models, see AI model routing.

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Spec sheet (API, 2026-08-24)

Spec	DeepSeek V4 Pro	GPT-5.6 Sol
Context window	1,048,576 tokens	1,050,000 tokens
Max output (if published)	Not published on the card we pulled	Not published on the card we pulled
Input modalities (card)	text	file, image, text
Output	text	text
Architecture note	MoE: 1.6T total parameters, 49B activated	Not stated on the card we pulled
Open weights	Not listed as open weights on the card we pulled	Not listed as open weights on the card we pulled
Vendor positioning (short)	Advanced reasoning and coding at 1M context	Flagship reasoning, coding, agents; command-line and multi-step coding

Context is a rounding-error near tie. Modalities are not. If your pipeline pastes screenshots or PDFs into the model, DeepSeek is the wrong ID unless you OCR first. We are not inventing leaderboard crowns for this pair.

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Pricing and real workload cost

List prices are easy to misread. Workload cost is what you feel. Rates below are from published API pricing on 2026-08-24.

Price	DeepSeek V4 Pro	GPT-5.6 Sol
Input / 1M tokens	$0.526	$2.00
Output / 1M tokens	$1.052	$10.00
Cache read / 1M	$0.044	$0.20

DeepSeek input is about one-quarter of Sol ($0.53 vs $2). Output is about one-tenth ($1.05 vs $10). Cache reads are about one-fifth ($0.044 vs $0.20).

Scenario	Assumed tokens	Est. DeepSeek V4 Pro	Est. GPT-5.6 Sol
Chat turn	1k in + 0.5k out	$0.0011	$0.007
Repo / doc review	80k in + 4k out	$0.046	$0.20
Agent loop	200k in (50% cached) + 20k out	$0.078	$0.42

A thousand cached agent loops are about $78 on DeepSeek vs about $420 on Sol at these list rates. That is the volume argument. It does not buy you image or file input. For subscription stacks, see AI subscription stack cost.

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Performance by job (specs + live pack)

No invented benches. Signals: the API card, and three live prompts. Method: side-by-side AI comparison.

Coding and agents

Both cards mention advanced / complex coding. Sol’s vendor line adds command-line and multi-step coding. Our empty-list test: both named ZeroDivisionError and guarded before dividing. DeepSeek returned 0.0. Sol returned 0 and printed average([]). Correctness tie. Sol is the slightly more “did you run it?” writeup. DeepSeek is the cheaper loop for text-only agents. Do not send screenshots of a failing UI to DeepSeek on this card.

Writing and tone

DeepSeek: “I hope you’re having a good week,” Q3 deck, finance numbers promised Friday and still missing, move to next week (maybe Wednesday), competitive slide still needs Acme pricing, “Thanks so much, and let me know your thoughts.” Sol: same facts, no week-hope greeting, “next Wednesday,” short “Thanks!” Both kept Acme pricing. Sol is tighter. DeepSeek is friendlier and a bit more padded.

Research, math, reasoning

This is the split that should change routing. DeepSeek correctly said the Moon is not green cheese (silicate minerals, metals, inorganic). Then it humored the hypothetical and started a “site selection and sampling” plan as if the surface were cheese-like protein ore. Sol refused, denied mineable protein, and planned polar water ice, purification, habitat protein, and recycle. Sol passes the “do not build on a false world” bar more cleanly. DeepSeek flags the falsehood, then collaborates with it. That pattern is dangerous in research agents.

Multimodal and long context

Context: 1,048,576 vs 1,050,000. Ignore the 1.4k token delta. Modalities: DeepSeek text only. Sol file, image, text. If the job is PDF packs or screenshots, Sol (or another multimodal ID) is the primary. DeepSeek can still summarize text you extract yourself.

Speed

Not measured in this pack. Measure p50 from your region. Do not budget latency from a screenshot.

Job	Edge	Why
Hard coding / agents	Split: Sol if files/CLI; DeepSeek if cheap text	Micro-fix tie; Sol vendor line on multi-step coding
Everyday writing	GPT-5.6 Sol (often)	Tighter complete letter; DeepSeek warmer padding
Long docs / files / images	GPT-5.6 Sol	File + image on the card; DeepSeek is text only
False-premise handling	GPT-5.6 Sol	Refuses and plans real resources; DeepSeek plays along after the flag
Cost at volume	DeepSeek V4 Pro	Several times cheaper on chat, repo, and cached agent loops

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Side-by-side test (live API test, 2026-08-24)

We ran the same prompts on DeepSeek V4 Pro and GPT-5.6 Sol in a multi-model workspace. Three live prompts in this batch. Editorial scores 1-5 on instruction following, depth, factual caution, style, and usefulness (max 25 per prompt).

Test 1: Client email rewrite

Task: Keep every fact. Warmer. Under 120 words.

DeepSeek V4 Pro (excerpt): Greeting week-hope, Q3 deck from last Tuesday, finance still missing after Friday, ask to move to next week (maybe Wednesday), competitive slide still needs Acme pricing, thanks and “let me know your thoughts.”

GPT-5.6 Sol (excerpt): Direct follow-up, Finance numbers expected Friday and not received, next Wednesday if it works, competitive slide plus Acme pricing, “Thanks!”

Edge: Sol for tightness. DeepSeek for warmth. Both kept the Acme fact. If your brand hates extra scaffolding, Sol.

Test 2: Empty-list average bug

Both named ZeroDivisionError and guarded with if not nums. DeepSeek returns 0.0. Sol returns 0 and prints the empty call. Tie on correctness. Sol slightly more operational.

Test 3: False premise (Moon cheese)

DeepSeek: premise false, rocky silicates and metals. Then: “if we humor the hypothetical” and a practical plan starting at site selection and sampling of “green cheese.” Sol: premise false, no mineable protein, four real resource steps (polar ice, purify/split, habitat protein, recycle). Sol wins factual caution. DeepSeek’s after-the-flag collaboration is the failure mode to watch. Dual-check publishable claims: multi-model hallucination checks.

Prompt	DeepSeek V4 Pro	GPT-5.6 Sol	Note
Email rewrite	22/25	23/25	Both complete; Sol tighter, DeepSeek warmer
Bug fix	23/25	24/25	Both correct; Sol adds a print
False premise	20/25	24/25	DeepSeek flags, then humors cheese mining
Total (this pack)	65/75	71/75	Gap is mostly trust, not grammar

Use DeepSeek where the prompt is boring, text-only, and cheap. Escalate to Sol (or another careful model) when the user might be wrong, the input is a file, or the output will be published.

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Ecosystem and where you run them

• DeepSeek V4 Pro: DeepSeek API and DeepSeek products. Strength: MoE scale at low list rates, 1M-class text context. Limit: text in, text out on the card we pulled.
• GPT-5.6 Sol: OpenAI API and ChatGPT. Strength: file/image/text, flagship GPT-5.6 lane, command-line / multi-step coding copy.
• Both in one place: i10X lets you keep the cheap text ID and the multimodal ID on the same prompt. See the multi-model AI guide.

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Pros, cons, and failure modes

DeepSeek V4 Pro

• Pros: ~1.05M context; MoE 1.6T / 49B activated on the card; very low list rates; complete friendly email in our capture; correct empty-list guard.
• Cons: Text input only; output is cheap but you still need a critic; false-premise answer collaborated after the flag; no file/image on this card.
• Fails when: the next step is a PDF, a screenshot, or a user who is confidently wrong. Cheap tokens will happily extend a false world.

GPT-5.6 Sol

• Pros: File + image + text; 1.05M context; tighter email; smoke-test print; refuse-and-redirect on the false premise; vendor line on agents and CLI coding.
• Cons: Several times more expensive on our workloads; not the lowest bill in the GPT-5.6 family if a cheaper sibling exists (test separately).
• Fails when: you burn Sol on high-volume text that DeepSeek already handles, and you never cache.

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Decision guide: pick one or route both

If you need…	Choose
Cheap 1M-class text volume	DeepSeek V4 Pro
PDF / screenshot / image in the prompt	GPT-5.6 Sol
Tight ops email	GPT-5.6 Sol (A/B once)
Warm draft at low spend	DeepSeek V4 Pro (A/B once)
Refuse-first research agents	GPT-5.6 Sol in this pack
Mixed SaaS week	Both: text volume → DeepSeek; files, images, trust → Sol

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Application walkthroughs: where each model is better

1) Customer support email

Better often: GPT-5.6 Sol for a short complete letter. DeepSeek is a fine volume drafter if you like the extra warmth and you edit the close. Both kept Acme pricing in this capture.

2) Long PDF / research pack

Better: GPT-5.6 Sol on the card, because it accepts files. DeepSeek can only see text you extract. If you already have a parser, DeepSeek is the cheaper summarizer, with a Sol critic on the output. Method: side-by-side AI comparison.

3) Everyday Python scripting

Split. Micro-fix was a near tie. Sol added a print and carries the CLI / multi-step vendor line. DeepSeek is the cheaper text coding loop. Do not paste terminal screenshots into DeepSeek on this card.

4) False premise and trust

Better: GPT-5.6 Sol. DeepSeek’s “humor the hypothetical” turn is the lesson. Flag-then-collaborate is not the same as refuse-and-redirect.

5) Output-heavy generation at API scale

Better on cost: DeepSeek V4 Pro for text. Agent-loop estimate $0.078 vs $0.42. Keep Sol for the steps that need files or caution.

6) MoE-scale text reasoning

DeepSeek V4 Pro is the card that publishes 1.6T total / 49B activated. That is architecture context, not a live score. Use it to explain the cost, not to skip evaluation.

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Consumer plans vs API (do not mix them up)

• API comparison (this article): DeepSeek V4 Pro vs GPT-5.6 Sol.
• Consumer apps: DeepSeek chat vs ChatGPT may wrap different tools, file uploaders, and bundled smaller models.

Phone UX is a lived week in each app. Agent IDs are this page.

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What this means for routing

• Text-only volume → DeepSeek V4 Pro
• Files, images, screenshots → GPT-5.6 Sol
• User might be wrong / publishable claim → Sol (or another critic), not DeepSeek alone
• Email → A/B; Sol won tightness here

Related: DeepSeek V4 Pro vs Claude Opus 5, Claude Fable 5 vs GPT-5.6 Sol. Playbook: AI model routing.

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Frequently asked questions

Which is better overall, DeepSeek V4 Pro or GPT-5.6 Sol?
Neither. DeepSeek wins text cost. Sol wins modalities and false-premise discipline in this pack (71/75 vs 65/75, gap mostly the cheese-mine turn).

Which is better for coding?
Near tie on our empty-list patch. Sol added a print and is positioned for command-line / multi-step work. DeepSeek is the cheaper text loop. Sol if the context includes files.

Which is better for writing?
Sol was tighter. DeepSeek was warmer. Both kept the facts in our capture. A/B on voice.

Which is cheaper?
DeepSeek V4 Pro, at published API rates (2026-08-24): about $0.53 / $1.05 / $0.044 cache vs Sol $2 / $10 / $0.20. Workloads: $0.0011 vs $0.007 chat, $0.046 vs $0.20 repo, $0.078 vs $0.42 cached agent loop.

Which has the larger context window?
Near tie. Sol 1,050,000 vs DeepSeek 1,048,576 on the cards we pulled.

Do I need both?
If you mix cheap text volume with PDFs or trust-sensitive answers, yes.

Are we comparing apps or API models?
API models DeepSeek V4 Pro (listed build 0423) and GPT-5.6 Sol.

How often should I re-test?
After version bumps. Monthly is sane. Always re-run a false-premise trap on the cheap model.

Where can I run them side by side?
i10X. Method: side-by-side AI comparison.

What about hallucinations and trust?
DeepSeek labeled the premise false, then built on it. Sol did not. Use a second-model check. Multi-model hallucination checks.

Can I OCR first and still use DeepSeek?
Yes, as a text summarizer. You then own parsing errors. Sol can take the file directly on this card.

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