Claude Opus 4.7 vs DeepSeek V4

Anthropic · US  |  DeepSeek · China · Updated June 2026

Quick verdict

Pick Claude Opus 4.7 for long-running agentic coding workflows or precise instruction following. Pick DeepSeek V4 for near-frontier coding at ~1/12 the cost or open mit-licensed weights you can self-host. Choose DeepSeek V4 if you need self-hosting or data privacy; Claude Opus 4.7 if you want a managed API.

Claude Opus 4.7 (Anthropic, US) and DeepSeek V4 (DeepSeek, China) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. Claude Opus 4.7 is the agentic-coding-focused Opus that traded some long-context recall for long-run reliability. DeepSeek V4 is china's open-weight price earthquake — near-frontier capability at roughly a twelfth of GPT-5.5's cost. They diverge most on price and open vs. closed weights — each quantified below from the models' real specs.

Key differences at a glance

Side-by-side specs

SpecClaude Opus 4.7DeepSeek V4
ProviderAnthropic (US) DeepSeek (China)
ReleasedApril 2026 April 24, 2026
Context window1M (~1,500 pages) 1M (~1,500 pages)
Price (in/out)$5/$25 per 1M tokens $0.435/$0.87 per 1M tokens
Open weight?No — API only Yes — self-hostable
Modalitiestext, image, code text, code
SWE-Bench VerifiedNot published 80.6%
MRCR v2 @ 1M32.2% Not published

Who wins what

Long-running agentic coding workflows

Claude Opus 4.7

A core design strength of Claude Opus 4.7.

Precise instruction following

Claude Opus 4.7

A core design strength of Claude Opus 4.7.

Task budgets and effort tiers

Claude Opus 4.7

A core design strength of Claude Opus 4.7.

Near-frontier coding at ~1/12 the cost

DeepSeek V4

A core design strength of DeepSeek V4.

Open MIT-licensed weights you can self-host

DeepSeek V4

A core design strength of DeepSeek V4.

No long-context surcharge

DeepSeek V4

A core design strength of DeepSeek V4.

Lowest cost at scale

DeepSeek V4

At $0.435/$0.87 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.

Which should you pick?

A cost-sensitive startup shipping high volume

DeepSeek V4

At $0.435/$0.87 per 1M tokens it undercuts Claude Opus 4.7, and on millions of tokens that margin decides the monthly bill.

A team with data-privacy or self-hosting needs

DeepSeek V4

Open weights let you run it on your own hardware; Claude Opus 4.7 is API-only.

Anyone whose priority is long-running agentic coding workflows

Claude Opus 4.7

It is specifically built for that.

Anyone whose priority is near-frontier coding at ~1/12 the cost

DeepSeek V4

That is its strongest area.

An enterprise with regional data-residency rules

Claude Opus 4.7 or DeepSeek V4

Origin (US vs China) affects where data is processed and which compliance regime applies — check the provider's terms for your region.

Claude Opus 4.7: where it fits

The agentic-coding-focused Opus that traded some long-context recall for long-run reliability. Released April 2026 by Anthropic, it is built for long-running agentic coding workflows, precise instruction following, task budgets and effort tiers, and large-codebase operation.

Its trade-offs are real: long-context recall regressed vs 4.6, and superseded by Opus 4.8. At $5 in / $25 out per million tokens, it sits in the premium price band.

DeepSeek V4: where it fits

China's open-weight price earthquake — near-frontier capability at roughly a twelfth of GPT-5.5's cost. Released April 24, 2026 by DeepSeek, it is built for near-frontier coding at ~1/12 the cost, open MIT-licensed weights you can self-host, no long-context surcharge, and highest LiveCodeBench result.

Its trade-offs: trails the very best on hardest agentic coding, and text/code focused, less multimodal. At $0.435 in / $0.87 out per million tokens, it sits in the budget price band.

The bottom line for this matchup

The defining split here is open vs. closed. DeepSeek V4 gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Claude Opus 4.7 gives you a managed, always-updated API with no infrastructure to run. Teams with GPUs, privacy requirements, or huge volume often favour the open model; teams that want zero ops and the latest capabilities favour the closed one. Capability is close enough that this operational question, not the benchmark, usually decides it.

Want both Claude Opus 4.7 and DeepSeek V4 without two subscriptions? LumiChats gives you these plus 40+ models under one ₹69/day pass (about $1/day) — draft with one, cross-check with the other.

See pricing

Frequently asked questions

Is Claude Opus 4.7 or DeepSeek V4 better for coding?

Public SWE-Bench figures are not available for Claude Opus 4.7, so the honest test is your own repository — run an identical real bug through both. By design, Claude Opus 4.7 leans toward long-running agentic coding workflows while DeepSeek V4 leans toward near-frontier coding at ~1/12 the cost, and that positioning usually predicts which feels better on your codebase.

Which is cheaper, Claude Opus 4.7 or DeepSeek V4?

DeepSeek V4 is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Claude Opus 4.7 is API-metered at $5/$25 per 1M tokens. For most teams without GPUs, the API model is cheaper to start; at very high volume, self-hosting can win.

Which has the bigger context window?

Both advertise 1M (~1,500 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.

Can I use both Claude Opus 4.7 and DeepSeek V4 together?

Yes — a multi-model platform like LumiChats gives you Claude Opus 4.7, DeepSeek V4 and 40+ others under one ₹69/day pass (about $1/day), so you can draft with one and cross-check with the other instead of buying two subscriptions.

Which is newer, Claude Opus 4.7 or DeepSeek V4?

DeepSeek V4 — released April 24, 2026, about 7 days after Claude Opus 4.7.

Related comparisons

Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.