DeepSeek V4-Pro vs IBM Granite 4.1

DeepSeek · China  |  IBM · US · Updated June 2026

Quick verdict

Pick DeepSeek V4-Pro for open-weight 1.6t moe (~49b active) — deepseek's largest, self-hostable or 1m-token context with up to 384k output tokens. Pick IBM Granite 4.1 for enterprise-grade open weights - apache 2.0, iso 42001-certified, cryptographically signed or efficient hybrid mamba-2/transformer design - much lower memory and faster inference. On a tight budget at scale, IBM Granite 4.1 is the value pick.

DeepSeek V4-Pro (DeepSeek, China) and IBM Granite 4.1 (IBM, US) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. DeepSeek V4-Pro is deepSeek's 1.6-trillion-parameter open-weight model at $0.435/$0.87 — strong open-weight coding and a 1M context at a fraction of flagship prices. IBM Granite 4.1 is iBM's most complete open-weight enterprise release - small, Apache-2.0, long-context and governance-friendly, built to deploy efficiently rather than top the leaderboard. They diverge most on price and context window — each quantified below from the models' real specs.

Key differences at a glance

Side-by-side specs

SpecDeepSeek V4-ProIBM Granite 4.1
ProviderDeepSeek (China) IBM (US)
ReleasedApril 24, 2026 April 29, 2026
Context window1M (~1,500 pages) 512K (~768 pages)
Price (in/out)$0.435/$0.87 per 1M tokens Open weight (self-host / free)
Open weight?Yes — self-hostable Yes — self-hostable
Modalitiestext, code text, code
SWE-Bench VerifiedNot published Not published
MRCR v2 @ 1MNot published Not published

Who wins what

Open-weight 1.6T MoE (~49B active) — DeepSeek's largest, self-hostable

DeepSeek V4-Pro

DeepSeek's 1.6-trillion-parameter open-weight model at $0.435/$0.87 — strong open-weight coding and a 1M context at a fraction of flagship prices — and it carries the larger 1M context.

1M-token context with up to 384K output tokens

DeepSeek V4-Pro

Its 1M window holds about 2× more than IBM Granite 4.1's 512K in a single prompt.

Permanent low pricing at $0.435/$0.87 per million, set May 2026

DeepSeek V4-Pro

DeepSeek V4-Pro lists permanent low pricing at $0.435/$0.87 per million, set May 2026 among its strengths; IBM Granite 4.1 does not.

Enterprise-grade open weights - Apache 2.0, ISO 42001-certified, cryptographically signed

IBM Granite 4.1

IBM's most complete open-weight enterprise release - small, Apache-2.0, long-context and governance-friendly, built to deploy efficiently rather than top the leaderboard — and it is the newer of the two.

Efficient hybrid Mamba-2/transformer design - much lower memory and faster inference

IBM Granite 4.1

IBM Granite 4.1 lists efficient hybrid Mamba-2/transformer design - much lower memory and faster inference among its strengths; DeepSeek V4-Pro does not.

512K-token context on small, deployable dense models (3B/8B/30B)

IBM Granite 4.1

IBM Granite 4.1 lists 512K-token context on small, deployable dense models (3B/8B/30B) among its strengths; DeepSeek V4-Pro does not.

Lowest cost at scale

IBM Granite 4.1

Its weights are open, so at volume you pay for your own hardware instead of DeepSeek V4-Pro's $0.435/$0.87 per 1M tokens.

Largest single-prompt input

DeepSeek V4-Pro

Its 1M window is about 2× larger than IBM Granite 4.1's 512K, fitting roughly 1,500 pages in one prompt.

Which should you pick?

A cost-sensitive startup shipping high volume

IBM Granite 4.1

At Open weight (self-host / free) it undercuts DeepSeek V4-Pro, and on millions of tokens that margin decides the monthly bill.

Someone analysing very long documents or codebases

DeepSeek V4-Pro

Larger 1M window fits more in one prompt.

Anyone whose priority is open-weight 1.6t moe (~49b active) — deepseek's largest, self-hostable

DeepSeek V4-Pro

It is specifically built for that.

Anyone whose priority is enterprise-grade open weights - apache 2.0, iso 42001-certified, cryptographically signed

IBM Granite 4.1

That is its strongest area.

An enterprise with regional data-residency rules

IBM Granite 4.1 or DeepSeek V4-Pro

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

DeepSeek V4-Pro: where it fits

DeepSeek's 1.6-trillion-parameter open-weight model at $0.435/$0.87 — strong open-weight coding and a 1M context at a fraction of flagship prices. Released April 24, 2026 by DeepSeek, it is built for open-weight 1.6T MoE (~49B active) — DeepSeek's largest, self-hostable, 1M-token context with up to 384K output tokens, permanent low pricing at $0.435/$0.87 per million, set May 2026, and sources place it near the top of open-weight coding, around 80 on SWE-Bench Verified.

Its trade-offs are real: independent SWE-Bench Verified placement is inconsistent across sources, pro and Pro-Max variants are quoted with different scores, text and code only — no image, audio or video, and overlaps DeepSeek V4 and V3.2 already in this comparison. At $0.435 in / $0.87 out per million tokens, it sits in the budget price band.

IBM Granite 4.1: where it fits

IBM's most complete open-weight enterprise release - small, Apache-2.0, long-context and governance-friendly, built to deploy efficiently rather than top the leaderboard. Released April 29, 2026 by IBM, it is built for enterprise-grade open weights - Apache 2.0, ISO 42001-certified, cryptographically signed, efficient hybrid Mamba-2/transformer design - much lower memory and faster inference, 512K-token context on small, deployable dense models (3B/8B/30B), and free to self-host; governance-friendly for on-prem and regulated deployments.

Its trade-offs: not a frontier-intelligence competitor - built for efficient deployment, not top benchmark scores, best as a workhorse; reasoning-heavy tasks favor larger models, instruct models are text-focused (vision and speech are separate family members), and efficiency and performance claims are IBM's own. As an open-weight model, its running cost is your own hardware rather than a per-token fee.

The bottom line for this matchup

This is less "which is smarter" and more "which ecosystem fits." DeepSeek V4-Pro (China) and IBM Granite 4.1 (US) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. IBM Granite 4.1 is the cheaper option, which matters at volume. The pragmatic move is to run one real task through both and judge the outputs against your own constraints — including where your data is allowed to be processed.

Want both DeepSeek V4-Pro and IBM Granite 4.1 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 DeepSeek V4-Pro or IBM Granite 4.1 better for coding?

Public SWE-Bench figures are not available for either model, so the honest test is your own repository — run an identical real bug through both. By design, DeepSeek V4-Pro leans toward open-weight 1.6t moe (~49b active) — deepseek's largest, self-hostable while IBM Granite 4.1 leans toward enterprise-grade open weights - apache 2.0, iso 42001-certified, cryptographically signed, and that positioning usually predicts which feels better on your codebase.

Which is cheaper, DeepSeek V4-Pro or IBM Granite 4.1?

IBM Granite 4.1 is cheaper — $0.435/$0.87 per 1M tokens vs Open weight (self-host / free).

Which has the bigger context window?

DeepSeek V4-Pro — 1M vs 512K, about 2× larger. Useful only if the model actually reasons over the full window, which not all do.

Can I use both DeepSeek V4-Pro and IBM Granite 4.1 together?

Yes — a multi-model platform like LumiChats gives you DeepSeek V4-Pro, IBM Granite 4.1 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, DeepSeek V4-Pro or IBM Granite 4.1?

IBM Granite 4.1 — released April 29, 2026, about 5 days after DeepSeek V4-Pro.

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.