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 Mistral Large 3 for open-weight (apache 2.0), self-hostable or strong multilingual performance. On a tight budget at scale, DeepSeek V4-Pro is the value pick.
DeepSeek V4-Pro (DeepSeek, China) and Mistral Large 3 (Mistral, France) 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. Mistral Large 3 is france's frontier contender — strong multilingual model with European data residency. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences
Price: nearly identical — $0.435/$0.87 per 1M tokens vs $0.5/$1.5 per 1M tokens. Cost will not be the deciding factor here.
Context window: DeepSeek V4-Pro holds 3.9× more — 1M (~1,500 pages) vs 256K (~384 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
Recency: DeepSeek V4-Pro is the newer model by about 5 months (released April 24, 2026), usually meaning fresher training data and capabilities.
Ecosystem: this is a China-vs-France matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Specifications
Spec
DeepSeek V4-Pro
Mistral Large 3
Provider
DeepSeek (China)
Mistral (France)
Released
April 24, 2026
December 2, 2025
Context window
1M (~1,500 pages)
256K (~384 pages)
Price (in/out)
$0.435/$0.87 per 1M tokens
$0.5/$1.5 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, image, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not 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 runs cheaper at $0.435/$0.87 per 1M tokens.
1M-token context with up to 384K output tokens: DeepSeek V4-Pro — Its 1M window holds about 3.9× more than Mistral Large 3's 256K in a single prompt.
Permanent low pricing at $0.435/$0.87 per million, set May 2026: 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.
Open-weight (Apache 2.0), self-hostable: Mistral Large 3 — Mistral Large 3 lists open-weight (Apache 2.0), self-hostable among its strengths; DeepSeek V4-Pro does not.
Strong multilingual performance: Mistral Large 3 — Mistral Large 3 lists strong multilingual performance among its strengths; DeepSeek V4-Pro does not.
Efficient inference: Mistral Large 3 — Mistral Large 3 lists efficient inference among its strengths; DeepSeek V4-Pro does not.
Lowest cost at scale: DeepSeek V4-Pro — At $0.435/$0.87 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input: DeepSeek V4-Pro — Its 1M window is about 3.9× larger than Mistral Large 3's 256K, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: DeepSeek V4-Pro — At $0.435/$0.87 per 1M tokens it undercuts Mistral Large 3, 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 open-weight (apache 2.0), self-hostable: Mistral Large 3 — That is its strongest area.
An enterprise with regional data-residency rules: Mistral Large 3 or DeepSeek V4-Pro — Origin (China vs France) 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.
Mistral Large 3: where it fits
France's frontier contender — strong multilingual model with European data residency. Released December 2, 2025 by Mistral, it is built for open-weight (Apache 2.0), self-hostable, strong multilingual performance, efficient inference, and function calling.
Its trade-offs: smaller context than US/China frontier, and less benchmark coverage. At $0.5 in / $1.5 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
This is less "which is smarter" and more "which ecosystem fits." DeepSeek V4-Pro (China) and Mistral Large 3 (France) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. DeepSeek V4-Pro 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.
Frequently asked questions
Is DeepSeek V4-Pro or Mistral Large 3 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 Mistral Large 3 leans toward open-weight (apache 2.0), self-hostable, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, DeepSeek V4-Pro or Mistral Large 3?
DeepSeek V4-Pro is cheaper — $0.435/$0.87 per 1M tokens vs $0.5/$1.5 per 1M tokens, roughly 1.1× apart on input.
Which has the bigger context window?
DeepSeek V4-Pro — 1M vs 256K, about 3.9× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both DeepSeek V4-Pro and Mistral Large 3 together?
Yes — a multi-model platform like LumiChats gives you DeepSeek V4-Pro, Mistral Large 3 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 Mistral Large 3?
DeepSeek V4-Pro — released April 24, 2026, about 5 months after Mistral Large 3.
DeepSeek V4-Pro vs Mistral Large 3
DeepSeek · China | Mistral · France · 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 Mistral Large 3 for open-weight (apache 2.0), self-hostable or strong multilingual performance. On a tight budget at scale, DeepSeek V4-Pro is the value pick.
DeepSeek V4-Pro (DeepSeek, China) and Mistral Large 3 (Mistral, France) 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. Mistral Large 3 is france's frontier contender — strong multilingual model with European data residency. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences at a glance
▸Price: nearly identical — $0.435/$0.87 per 1M tokens vs $0.5/$1.5 per 1M tokens. Cost will not be the deciding factor here.
▸Context window: DeepSeek V4-Pro holds 3.9× more — 1M (~1,500 pages) vs 256K (~384 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
▸Recency: DeepSeek V4-Pro is the newer model by about 5 months (released April 24, 2026), usually meaning fresher training data and capabilities.
▸Ecosystem: this is a China-vs-France matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
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 runs cheaper at $0.435/$0.87 per 1M tokens.
1M-token context with up to 384K output tokens
DeepSeek V4-Pro
Its 1M window holds about 3.9× more than Mistral Large 3's 256K in a single prompt.
Permanent low pricing at $0.435/$0.87 per million, set May 2026
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.
Open-weight (Apache 2.0), self-hostable
Mistral Large 3
Mistral Large 3 lists open-weight (Apache 2.0), self-hostable among its strengths; DeepSeek V4-Pro does not.
Strong multilingual performance
Mistral Large 3
Mistral Large 3 lists strong multilingual performance among its strengths; DeepSeek V4-Pro does not.
Efficient inference
Mistral Large 3
Mistral Large 3 lists efficient inference among its strengths; DeepSeek V4-Pro does not.
Lowest cost at scale
DeepSeek V4-Pro
At $0.435/$0.87 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input
DeepSeek V4-Pro
Its 1M window is about 3.9× larger than Mistral Large 3's 256K, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ DeepSeek V4-Pro
At $0.435/$0.87 per 1M tokens it undercuts Mistral Large 3, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
Anyone whose priority is open-weight (apache 2.0), self-hostable
→ Mistral Large 3
That is its strongest area.
An enterprise with regional data-residency rules
→ Mistral Large 3 or DeepSeek V4-Pro
Origin (China vs France) 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.
Mistral Large 3: where it fits
France's frontier contender — strong multilingual model with European data residency. Released December 2, 2025 by Mistral, it is built for open-weight (Apache 2.0), self-hostable, strong multilingual performance, efficient inference, and function calling.
Its trade-offs: smaller context than US/China frontier, and less benchmark coverage. At $0.5 in / $1.5 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
This is less "which is smarter" and more "which ecosystem fits." DeepSeek V4-Pro (China) and Mistral Large 3 (France) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. DeepSeek V4-Pro 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 Mistral Large 3 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.
Is DeepSeek V4-Pro or Mistral Large 3 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 Mistral Large 3 leans toward open-weight (apache 2.0), self-hostable, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, DeepSeek V4-Pro or Mistral Large 3?
DeepSeek V4-Pro is cheaper — $0.435/$0.87 per 1M tokens vs $0.5/$1.5 per 1M tokens, roughly 1.1× apart on input.
Which has the bigger context window?
DeepSeek V4-Pro — 1M vs 256K, about 3.9× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both DeepSeek V4-Pro and Mistral Large 3 together?
Yes — a multi-model platform like LumiChats gives you DeepSeek V4-Pro, Mistral Large 3 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 Mistral Large 3?
DeepSeek V4-Pro — released April 24, 2026, about 5 months after Mistral Large 3.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.