DeepSeek V4-Flash vs MiniMax M3
DeepSeek · China | MiniMax · China · Updated June 2026
Quick verdict
Pick DeepSeek V4-Flash for exceptional value — artificial analysis intelligence index 52 at just $0.14/$0.28 per million tokens or mit-licensed open weights — free to self-host or run via a western host. Pick MiniMax M3 for open-weight 428b moe (~23b active per token) with a 1m-token context or native multimodal input — text, image and video. On a tight budget at scale, DeepSeek V4-Flash is the value pick.
DeepSeek V4-Flash (DeepSeek) and MiniMax M3 (MiniMax) are two of the models people most often weigh against each other in 2026. DeepSeek V4-Flash is deepSeek's MIT-licensed open-weight model — arguably the best value-per-intelligence available, scoring 52 on the independent AA Index at just $0.14/$0.28 per million tokens. MiniMax M3 is miniMax's open-weight 428B multimodal model with a 1M context — strong reported coding (59.0 SWE-Bench Pro) at low entry pricing. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences at a glance
- ▸Price: nearly identical — $0.22/$0.66 per 1M tokens vs $0.23/$0.96 per 1M tokens. Cost will not be the deciding factor here.
- ▸Context window: 1M vs 1M — within a few percent of each other, so treat this as a tie and test on your own long inputs, since usable recall varies by model.
- ▸Recency: DeepSeek V4-Flash is the newer model by about 2 months (released July 31, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
| Spec | DeepSeek V4-Flash | MiniMax M3 |
|---|---|---|
| Provider | DeepSeek (China) | MiniMax (China) |
| Released | July 31, 2026 | May 31, 2026 |
| Context window | 1M (~1,500 pages) | 1M (~1,573 pages) |
| Price (in/out) | $0.22/$0.66 per 1M tokens | $0.23/$0.96 per 1M tokens |
| Open weight? | Yes — self-hostable | Yes — self-hostable |
| Modalities | text, code | text, image, video, code |
| SWE-Bench Verified | Not published | Not published |
| MRCR v2 @ 1M | Not published | Not published |
Who wins what
Exceptional value — Artificial Analysis Intelligence Index 52 at just $0.14/$0.28 per million tokens
DeepSeek V4-Flash
At $0.22/$0.66 per 1M tokens it undercuts MiniMax M3 ($0.23/$0.96 per 1M tokens), and that gap compounds at volume.
MIT-licensed open weights — free to self-host or run via a Western host
DeepSeek V4-Flash
DeepSeek's MIT-licensed open-weight model — arguably the best value-per-intelligence available, scoring 52 on the independent AA Index at just $0.14/$0.28 per million tokens — and it runs cheaper at $0.22/$0.66 per 1M tokens.
1M-token context window
DeepSeek V4-Flash
MiniMax M3 is comparatively weak here — price doubles to $0.60/$2.40 above 512K tokens — not flat across 1M
Open-weight 428B MoE (~23B active per token) with a 1M-token context
MiniMax M3
MiniMax M3 lists open-weight 428B MoE (~23B active per token) with a 1M-token context among its strengths; DeepSeek V4-Flash does not.
Native multimodal input — text, image and video
MiniMax M3
DeepSeek V4-Flash is comparatively weak here — text and code focused — not a full multimodal model
Reports 59.0 on SWE-Bench Pro (a strong open-weight score), surpassing GPT-5.5
MiniMax M3
DeepSeek V4-Flash is comparatively weak here — coding/agentic figures like Terminal-Bench 82.7 are DeepSeek's own, not independently reproduced
Lowest cost at scale
DeepSeek V4-Flash
At $0.22/$0.66 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-Flash
At $0.22/$0.66 per 1M tokens it undercuts MiniMax M3, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ MiniMax M3
Larger 1M window fits more in one prompt.
Anyone whose priority is exceptional value — artificial analysis intelligence index 52 at just $0.14/$0.28 per million tokens
→ DeepSeek V4-Flash
It is specifically built for that.
Anyone whose priority is open-weight 428b moe (~23b active per token) with a 1m-token context
→ MiniMax M3
That is its strongest area.
DeepSeek V4-Flash: where it fits
DeepSeek's MIT-licensed open-weight model — arguably the best value-per-intelligence available, scoring 52 on the independent AA Index at just $0.14/$0.28 per million tokens. Released July 31, 2026 by DeepSeek, it is built for exceptional value — Artificial Analysis Intelligence Index 52 at just $0.14/$0.28 per million tokens, mIT-licensed open weights — free to self-host or run via a Western host, 1M-token context window, and strong coding and agentic performance for the price (DeepSeek reports 82.7 on Terminal-Bench 2.1).
Its trade-offs are real: coding/agentic figures like Terminal-Bench 82.7 are DeepSeek's own, not independently reproduced, text and code focused — not a full multimodal model, deepSeek's own hosted API stores data in China; self-host or use a Western host for privacy, and below the top frontier models on overall intelligence. At $0.22 in / $0.66 out per million tokens, it sits in the budget price band.
MiniMax M3: where it fits
MiniMax's open-weight 428B multimodal model with a 1M context — strong reported coding (59.0 SWE-Bench Pro) at low entry pricing. Released May 31, 2026 by MiniMax, it is built for open-weight 428B MoE (~23B active per token) with a 1M-token context, native multimodal input — text, image and video, reports 59.0 on SWE-Bench Pro (a strong open-weight score), surpassing GPT-5.5, and low entry pricing at $0.30/$1.20 per million up to 512K tokens.
Its trade-offs: price doubles to $0.60/$2.40 above 512K tokens — not flat across 1M, miniMax's own reported 80.5 SWE-Bench Verified figure is vendor-stated, not independently verified, sWE-Bench Pro is a different, harder benchmark than SWE-Bench Verified, and newer than M2.7 but with less independent testing so far. At $0.23 in / $0.96 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
DeepSeek V4-Flash and MiniMax M3 overlap enough that the right pick depends on your specific job. DeepSeek V4-Flash costs less per token; MiniMax M3 holds the larger context; and each leads in its own area — DeepSeek V4-Flash for exceptional value — artificial analysis intelligence index 52 at just $0.14/$0.28 per million tokens, MiniMax M3 for open-weight 428b moe (~23b active per token) with a 1m-token context. Rather than crowning one, run the same hard task through both once and let the results decide.
Want both DeepSeek V4-Flash and MiniMax M3 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-Flash or MiniMax M3 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-Flash leans toward exceptional value — artificial analysis intelligence index 52 at just $0.14/$0.28 per million tokens while MiniMax M3 leans toward open-weight 428b moe (~23b active per token) with a 1m-token context, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, DeepSeek V4-Flash or MiniMax M3?
DeepSeek V4-Flash is cheaper — $0.22/$0.66 per 1M tokens vs $0.23/$0.96 per 1M tokens, roughly 1× apart on input.
Which has the bigger context window?
Effectively neither — 1M vs 1M is a difference of a few percent. Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both DeepSeek V4-Flash and MiniMax M3 together?
Yes — a multi-model platform like LumiChats gives you DeepSeek V4-Flash, MiniMax M3 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-Flash or MiniMax M3?
DeepSeek V4-Flash — released July 31, 2026, about 2 months after MiniMax M3.
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.