DeepSeek V4 vs Mistral Large 4
DeepSeek · China | Mistral AI · France · Updated June 2026
Quick verdict
Pick DeepSeek V4 for near-frontier coding at ~1/12 the cost or open mit-licensed weights you can self-host. Pick Mistral Large 4 for mistral's largest model to date — 1.05t total parameters, 49b active per token (granular moe), plus a 1.6b-parameter vision encoder or trained from scratch on 3,800 nvidia grace blackwell gpus in mistral's own european data centers, across 160+ languages. Choose DeepSeek V4 if you need self-hosting or data privacy; Mistral Large 4 if you want a managed API.
DeepSeek V4 (DeepSeek, China) and Mistral Large 4 (Mistral AI, 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 is china's open-weight price earthquake — near-frontier capability at roughly a twelfth of GPT-5.5's cost. Mistral Large 4 is mistral's October 6, 2026 public preview of 'Le Chonk' — a 1.05T-parameter, natively multimodal MoE model with open weights planned by month's end. They diverge most on price and open vs. closed weights — each quantified below from the models' real specs.
Key differences at a glance
- ▸Price: nearly identical — $0.66/$1.98 per 1M tokens vs $0.68/$2.09 per 1M tokens. Cost will not be the deciding factor here.
- ▸Context window: both advertise 1M (~1,500 pages). Tie on paper — test on your own long inputs, since usable recall varies by model.
- ▸Recency: Mistral Large 4 is the newer model by about 6 months (released October 6, 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.
Side-by-side specs
| Spec | DeepSeek V4 | Mistral Large 4 |
|---|---|---|
| Provider | DeepSeek (China) | Mistral AI (France) |
| Released | April 24, 2026 | October 6, 2026 |
| Context window | 1M (~1,500 pages) | 1M tokens (~1,500 pages) |
| Price (in/out) | $0.66/$1.98 per 1M tokens | $0.68/$2.09 per 1M tokens |
| Open weight? | Yes — self-hostable | No — API only |
| Modalities | text, code | text, image, code |
| SWE-Bench Verified | 80.6% | Not published |
| MRCR v2 @ 1M | Not published | Not published |
Who wins what
Near-frontier coding at ~1/12 the cost
DeepSeek V4
At $0.66/$1.98 per 1M tokens it undercuts Mistral Large 4 ($0.68/$2.09 per 1M tokens), and that gap compounds at volume.
Open MIT-licensed weights you can self-host
DeepSeek V4
Open weights make this possible at all — Mistral Large 4 is API-only, so it cannot leave the vendor's servers.
No long-context surcharge
DeepSeek V4
China's open-weight price earthquake — near-frontier capability at roughly a twelfth of GPT-5.5's cost — and it runs cheaper at $0.66/$1.98 per 1M tokens.
Mistral's largest model to date — 1.05T total parameters, 49B active per token (granular MoE), plus a 1.6B-parameter vision encoder
Mistral Large 4
Mistral's October 6, 2026 public preview of 'Le Chonk' — a 1.05T-parameter, natively multimodal MoE model with open weights planned by month's end — and it is the newer of the two.
Trained from scratch on 3,800 Nvidia Grace Blackwell GPUs in Mistral's own European data centers, across 160+ languages
Mistral Large 4
Mistral Large 4 lists trained from scratch on 3,800 Nvidia Grace Blackwell GPUs in Mistral's own European data centers, across 160+ languages among its strengths; DeepSeek V4 does not.
Hybrid instruct/reasoning design — answers quickly by default or switches into step-by-step reasoning
Mistral Large 4
Mistral Large 4 lists hybrid instruct/reasoning design — answers quickly by default or switches into step-by-step reasoning among its strengths; DeepSeek V4 does not.
Lowest cost at scale
DeepSeek V4
At $0.66/$1.98 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 $0.66/$1.98 per 1M tokens it undercuts Mistral Large 4, 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; Mistral Large 4 is API-only.
Anyone whose priority is near-frontier coding at ~1/12 the cost
→ DeepSeek V4
It is specifically built for that.
Anyone whose priority is mistral's largest model to date — 1.05t total parameters, 49b active per token (granular moe), plus a 1.6b-parameter vision encoder
→ Mistral Large 4
That is its strongest area.
An enterprise with regional data-residency rules
→ Mistral Large 4 or DeepSeek V4
Origin (China vs France) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
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 are real: this generic 'V4' entry mirrors DeepSeek V4-Pro's specs - see DeepSeek V4-Pro and DeepSeek V4-Flash for DeepSeek's actual named product tiers, trails the very best on hardest agentic coding, and text/code focused, less multimodal. At $0.66 in / $1.98 out per million tokens, it sits in the budget price band.
Mistral Large 4: where it fits
Mistral's October 6, 2026 public preview of 'Le Chonk' — a 1.05T-parameter, natively multimodal MoE model with open weights planned by month's end. Released October 6, 2026 by Mistral AI, it is built for mistral's largest model to date — 1.05T total parameters, 49B active per token (granular MoE), plus a 1.6B-parameter vision encoder, trained from scratch on 3,800 Nvidia Grace Blackwell GPUs in Mistral's own European data centers, across 160+ languages, hybrid instruct/reasoning design — answers quickly by default or switches into step-by-step reasoning, and open weights planned by end of October 2026, which would make it among the largest open-weight models available.
Its trade-offs: still a public preview, not a finished release — Mistral says training is ongoing with 'no signs of saturation', weights not yet shipped as of mid-October 2026 — API-only for now despite the open-weight framing, launch pricing ($0.68/$2.09 per million) is a preview discount — Mistral's own model page lists a $1.36/$4.18 list price once the promotion ends, and scores 61.7% on DeepSWE v1.1, trailing DeepSeek V4.1 Flash (74.2%) and Kimi K3 (68.0%) on that coding benchmark. At $0.68 in / $2.09 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. Mistral Large 4 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 DeepSeek V4 and Mistral Large 4 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 pricingFrequently asked questions
Is DeepSeek V4 or Mistral Large 4 better for coding?
Public SWE-Bench figures are not available for Mistral Large 4, so the honest test is your own repository — run an identical real bug through both. By design, DeepSeek V4 leans toward near-frontier coding at ~1/12 the cost while Mistral Large 4 leans toward mistral's largest model to date — 1.05t total parameters, 49b active per token (granular moe), plus a 1.6b-parameter vision encoder, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, DeepSeek V4 or Mistral Large 4?
DeepSeek V4 is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Mistral Large 4 is API-metered at $0.68/$2.09 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 DeepSeek V4 and Mistral Large 4 together?
Yes — a multi-model platform like LumiChats gives you DeepSeek V4, Mistral Large 4 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 or Mistral Large 4?
Mistral Large 4 — released October 6, 2026, about 6 months after DeepSeek V4.
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.