MModelspectraIndependent AI Model Intelligence
Head-to-head · Updated 2026-09-08
中文
Model Comparison · Head to Head

DeepSeek-V4.1-Flash vs MiniMax M3

DeepSeek-V4.1-Flash wins on Overall, Coding, Multimodal. Every numeric field is compared below, with a worked monthly-cost example and a pick rule for each use case.

VendorDeepSeek / MiniMax
Data fields15+ dimensions
Updated2026-09-08
Read~9 min
01

Verdict at a glance

Bottom line: Choose DeepSeek-V4.1-Flash when coding depth, long-context reliability, lower refusal matter most; choose MiniMax M3 when its stronger dimensions is the priority.

Choose DeepSeek-V4.1-Flash if you…

  • 1M context at ~890 bytes/token global KV
  • Top agentic coding (DeepSWE 74.2, Terminal-Bench 2.1 90.6)
  • Persistent KV cut to ~1/8 via bounded replay
  • Open weights, extremely low serving cost
  • Best for: Long-context AI agents, Agentic coding, High-throughput deployment

Choose MiniMax M3 if you…

  • Strong agent ability
  • Open source
  • Low price
  • Best for: AI agents, Open source, Chinese
02

Head-to-head aggregate scores

Scores are 0–100, aggregated from public benchmark information and independently weighted across three leaderboards. Rank is out of 27 tracked models.

DeepSeek-V4.1-Flash Higher overall
DeepSeek · #12 overall
Overall76
Coding89
Multimodal78
VS
MiniMax M3
MiniMax · #17 overall
Overall70
Coding68
Multimodal70

Aggregated from public sources and independently weighted; methodology on the Terms page. Scores within 3 points are treated as statistically tied.

03

Specs & pricing — every field side by side

List API prices in USD per 1M tokens. The highlighted cell is the stronger value/capability on that row.

DimensionDeepSeek-V4.1-FlashMiniMax M3Verdict
VendorDeepSeek (CN)MiniMax (CN)Different vendors
Released2026.092026.06DeepSeek-V4.1-Flash is newer
Overall (rank)76 · #1270 · #17DeepSeek-V4.1-Flash +6
Coding89 · #568 · #18DeepSeek-V4.1-Flash +21
Multimodal78 · #1670 · #23DeepSeek-V4.1-Flash +8
Context window1M1MTie
Max output128K64KMiniMax M3 longer
Effective-context9890DeepSeek-V4.1-Flash more reliable
Input $/1M$0.14$0.6DeepSeek-V4.1-Flash cheaper
Output $/1M$0.28$2.4DeepSeek-V4.1-Flash cheaper
Cache discountnonenoneTie
Speed~85 tok/s~60 tok/sDeepSeek-V4.1-Flash faster
TTFT0.3s0.5sDeepSeek-V4.1-Flash snappier
Function calling8484Tie
Refusal rate~5%~7%DeepSeek-V4.1-Flash less restrictive
English8470DeepSeek-V4.1-Flash
Chinese8688MiniMax M3
Modalitiestext, imagetextdifferent coverage
Open weightsYesYesBoth open
Fine-tuningYesYes
Free tierDeepSeek App free; open weights to self-hostHailuo AI free; open-platform quota
SOC2 / no-trainno / yesno / yes
Private deploymentYesYesBoth support it

Fields drawn from vendor public documentation and the Modelspectra 27-model dataset; speed varies with network, concurrency and prompt length. Verify current pricing before purchase.

04

Dimension-by-dimension analysis

Reasoning & overall intelligence

DeepSeek-V4.1-Flash leads the overall aggregate by 6 points (76 vs 70). That is a meaningful, not marginal, edge on hard, multi-step reasoning, while MiniMax M3 remains a strong generalist that is not out of its depth on routine work.

Agentic coding

This is a decisive gap: DeepSeek-V4.1-Flash scores 89 against 68. On multi-file edits, SWE-style tickets and long-horizon agent loops DeepSeek-V4.1-Flash needs fewer correction turns; MiniMax M3 is still competent for scripts and assisted completion but trails as task complexity rises.

Multimodal

DeepSeek-V4.1-Flash leads multimodal 78 vs 70. A concrete modality difference: DeepSeek-V4.1-Flash additionally handles image. Neither emits native video, so the comparison is about parsing images and documents, not generation.

Speed & latency

DeepSeek-V4.1-Flash is faster in interactive use: ~85 tok/s with 0.3s TTFT versus ~60 tok/s with 0.5s TTFT (about 1.4× the throughput). For conversational UIs where perceived responsiveness drives retention, that edge is a real product factor even when raw reasoning is lower.

Context: window vs usable recall

Nominal windows are 1M for DeepSeek-V4.1-Flash and 1M for MiniMax M3. Effective-context scores point the same way as window size — DeepSeek-V4.1-Flash is ahead on usable recall (98 vs 90), so prefer it for long-document work where details cannot be missed.

Price & total cost

DeepSeek-V4.1-Flash is the cheaper API at $0.14/$0.28 versus MiniMax M3 at $0.6/$2.4 per 1M input/output tokens — list input is about 4.3× lower.

Chinese vs English

English: DeepSeek-V4.1-Flash 84 vs MiniMax M3 70. Chinese: 86 vs 88. For Chinese-language production, MiniMax M3 is the stronger pick. Note that non-Chinese models generally require overseas network access for their APIs.

Tool use & ecosystem

Function-calling score: DeepSeek-V4.1-Flash 84 vs MiniMax M3 84, so DeepSeek-V4.1-Flash has the edge on structured tool use. Fine-tuning is available from DeepSeek-V4.1-Flash and MiniMax M3. Factor in existing SDK/plugin familiarity — switching cost often outweighs a few-point tool-use gap.

05

Cost worked example — same workload, real token math

Assume a production workload of 100M input + 30M output tokens per month, with 90% of input tokens served from cache. Figures use public list prices.

Scenario · per monthDeepSeek-V4.1-FlashMiniMax M3Gap
List priceno cache applied$22100M in × $0.14  +  30M out × $0.28$132100M in × $0.6  +  30M out × $2.46.00×gap
With caching90% of inputs cache-hit$22no published cache discount$132no published cache discount6.00×gap

Illustrative model; your input/output mix and cache-hit ratio change the result. Prices are list rates before any enterprise agreement.

06

Decision tree

IF the workload is agentic or multi-file coding and a wrong first pass is expensive  →  choose DeepSeek-V4.1-Flash (coding 89 vs 68).
IF you serve real-time users and latency is a product KPI  →  choose DeepSeek-V4.1-Flash (~85 tok/s, 0.3s TTFT).
IF you are cost-driven at scale on routine, high-volume workloads  →  choose DeepSeek-V4.1-Flash ($$0.14/$$0.28 per 1M in/out).
IF long-document recall has to be near-perfect  →  choose DeepSeek-V4.1-Flash (effective context 98 vs 90).
07

Frequently asked questions

Is MiniMax M3 worth the higher price over DeepSeek-V4.1-Flash?
At list the input rate is 4.3x higher, but cache discounts and output pricing narrow the effective gap. Pay the premium when MiniMax M3's stronger dimensions protect revenue; for routine volume DeepSeek-V4.1-Flash is the economical pick.
Which is better for agentic coding?
DeepSeek-V4.1-Flash is decisively stronger for coding (89 vs 68 on the aggregate). The gap shows on SWE-style multi-file tasks and long agent loops that need fewer correction turns; MiniMax M3 is fine for routine scripts.
How do costs compare at 100M tokens/month with caching?
At 100M input + 30M output with 90% of inputs cache-hit, DeepSeek-V4.1-Flash is about $22/month and MiniMax M3 about $132/month after cache discounts ($22 and $132 at list).
Does the bigger context window actually matter?
Nominal windows are DeepSeek-V4.1-Flash (1M) and MiniMax M3 (1M), but usable recall follows the effective-context score (98 vs 90). Prefer the higher effective-context model for long-document work where nothing can be missed.
Which handles multimodal inputs better?
Multimodal scores are 78 (DeepSeek-V4.1-Flash) vs 70 (MiniMax M3), with modality coverage text/image versus text. Match the model to the input types your product actually receives.
What is the single-line recommendation?
Choose DeepSeek-V4.1-Flash for Long-context AI agents, Agentic coding; choose MiniMax M3 for AI agents, Open source.