MiniMax M3.1 Flash vs Deepseek V4.1: Benchmarks & Costs

Head-to-Head: MiniMax M3.1 Flash and DeepSeek V4.1 on Agent Automation and Cost

MiniMax M3.1 Flash delivers higher automation benchmark marks and a native 1,000,000-token context window, while DeepSeek V4.1 Flash maintains faster raw token throughput and higher composite reasoning indices—the right pick depends on whether your pipeline prioritizes autonomous token efficiency or raw generation speed.

The release of MiniMax M3.1 Flash on October 19, 2026, finalized the identity of the anonymous preview model previously known as Space Bunny. Over its stealth testing phase, Space Bunny processed tens of trillions of tokens on open model boards. Now officially deployed into MiniMax's primary API, M Plan, and developer tooling, it directly challenges DeepSeek V4.1 Flash (specifically the 1017 revision) across the budget reasoning sector.

Key Takeaways

• Price Parity on Base Tokens: MiniMax M3.1 Flash sets official pricing at $0.15 per million input tokens and $0.60 per million output tokens, matching DeepSeek V4.1 Flash off-peak rates.
• Cache Cost Differences: DeepSeek maintains an advantage in prompt caching economics, while MiniMax M3.1 Flash charges $0.015 per million cached input tokens.
• Automation vs. General Reasoning: MiniMax M3.1 Flash posts 62.2% on Automation-Bench against DeepSeek V4.1 Flash 1017's 60.5%, but DeepSeek scores higher on the Artificial Analysis Intelligence Index (45 vs. 42/43).
• Inference Speed Gap: DeepSeek V4.1 Flash leads pure output velocity, clocking up to 209 tokens per second on standardized benchmarks, compared to MiniMax M3.1 Flash's 90–110 tokens per second decode rate.
• Forced Reasoning: MiniMax M3.1 Flash enforces reasoning by default across five Tunable Thinking Depth levels (low, medium, high, xhigh, max); attempting to disable thinking triggers an API 400 error.
• Native Context Scope: MiniMax M3.1 Flash includes an official 1M-token context window supporting text, image, and video inputs.

MiniMax M3.1 Flash vs DeepSeek V4.1 at a Glance

Feature / Metric	MiniMax M3.1 Flash	DeepSeek V4.1 Flash (1017)	Verification Level
Developer / Lab	MiniMax	DeepSeek	Confirmed (Official docs)
Release / Reveal Date	October 19, 2026	October 17, 2026	Confirmed (Official tracking)
Architecture	MoE (Reported 428B total, ~23B active)	MoE (Unsloth 1-bit / sparse variants)	Community reported vs. unverified
Input Pricing (per 1M)	$0.15	$0.15 (off-peak match)	Confirmed (Official pricing)
Output Pricing (per 1M)	$0.60	$0.60 (off-peak match)	Confirmed (Official pricing)
Cache Read (per 1M)	$0.015	Lower than MiniMax (exact tier varies)	Confirmed comparison
Context Window	1,000,000 tokens (1M)	Unverified (varies by deployment)	Confirmed (MiniMax) vs. unverified
Multimodal Input	Text, Image (10MB), Video (512MB)	Text-centric (varies by deployment)	Confirmed (Official docs)
Artificial Analysis Index	42 – 43	45	Confirmed (Third-party evaluators)
Automation-Bench	62.2%	60.5%	Confirmed (Vendor benchmark table)
DeepSWE Score	73.7% – 74.0%	77.0%	Confirmed evaluations
Decode Throughput	90 – 110 t/s (benchmarked ~105 t/s)	Up to 209 t/s	Confirmed third-party telemetry
Reasoning Controls	5 effort tiers; cannot disable	Dynamic / endpoint dependent	Confirmed (MiniMax API docs)

Benchmark Performance and Intelligence Metrics

On composite intelligence benchmarks, DeepSeek V4.1 Flash maintains an edge, but MiniMax M3.1 Flash captures targeted wins on task automation and specialized evaluation sets.

According to Artificial Analysis evaluations, MiniMax M3.1 Flash achieved an Intelligence Index score of 42 to 43 at maximum reasoning effort, rising from 29 on the earlier MiniMax M3 architecture. DeepSeek V4.1 Flash 1017 sits slightly ahead on this aggregate scale at 45 points.

Source: @kimmonismus

On specific agentic capabilities, however, vendor disclosures reveal a different balance. On Automation-Bench v1.0.6, MiniMax M3.1 Flash registered 62.2% at max effort, edging out DeepSeek V4.1 Flash 1017's 60.5%. For software development, MiniMax reported 73.7% (with community sweeps citing 74%) on DeepSWE. While this exceeds older models, it trails DeepSeek V4.1 Flash 1017's reported 77% peak on software engineering evaluations. Independent testing on the Vals Index placed MiniMax M3.1 Flash at 53.78% accuracy, ranking it #15 of 22 evaluated models, driven by strong marks on Vibe Code Bench (73.94%) and Tax Agent Bench (32.14%).

Third-party testing highlighted nuanced trade-offs. VentureBeat noted that while MiniMax M3.1 Flash scored 48% on Humanity's Last Exam (surpassing DeepSeek V4.1 Flash 1017's 43%), evaluator testing flagged an elevated tendency for MiniMax to provide incorrect answers rather than decline unanswerable questions. Community evaluation by tester "Cat Boss" (猫老板) on the private Zhizhi benchmark showed MiniMax M3.1 Flash scoring a median of 51.76, trailing DeepSeek V4.1 Flash 1017's 56.03. In frontend aesthetic tasks, community leaderboards such as DesignArena placed DeepSeek V4.1 Flash ahead at 1319 points versus MiniMax M3.1 Flash at 1290 points.

Engineers evaluating how smaller cost-efficient models fit into multi-model architectures can review our analysis on DeepSeek V4.1 Flash vs Claude Opus 5.5 to understand frontier distillation patterns.

Speed, Throughput, and Real-World Latency

Raw generation throughput represents the clearest technical divergence between the two models. Telemetry from Artificial Analysis places DeepSeek V4.1 Flash 1017 at an average output velocity of 209 tokens per second. In contrast, MiniMax M3.1 Flash averaged approximately 105 tokens per second under similar conditions.

Real-world community trials confirm this gap. Independent developer testing conducted by AJ reported decode throughput between 90 and 110 tokens per second across multi-hour sessions using the MiniMax API.

Source: @ItsmeAjayKV

While DeepSeek generates tokens faster, total wall-clock time in complex agentic runs often hinges on token volume rather than raw generation speed. In side-by-side prompt executions observed by community members:

• Campfire Scene Generation: MiniMax M3.1 Flash concluded in 1 hour and 7 minutes using 8.1 million tokens ($0.22 total cost), while DeepSeek V4.1 Flash ran for 1 hour and 28 minutes, generating 89.5 million tokens ($0.54 total cost).
• Submarine Coding Prompt: Evaluated across iterative html building tasks, MiniMax completed in 29 minutes and 24 seconds ($0.21), compared to DeepSeek V4.1 Flash finishing in 27 minutes and 09 seconds ($0.38).

These figures demonstrate that MiniMax M3.1 Flash can compensate for lower raw throughput by requiring fewer redundant thinking tokens on specific multi-step code generation tasks.

Pricing Structures and Cache Economics

Both providers have aggressively lowered developer pricing, establishing a baseline of $0.15 per million input tokens and $0.60 per million output tokens for standard calls.

Standard Inference:
├── Input:  $0.15 / 1M tokens (Both models match off-peak)
└── Output: $0.60 / 1M tokens (Both models match off-peak)

Prompt Caching:
├── MiniMax M3.1 Flash:  $0.015 / 1M cached tokens (90% discount)
└── DeepSeek V4.1 Flash: Lower cache read fee (Leading ecosystem margins)

The economic divide sharpens around context reuse. MiniMax charges $0.015 per million tokens for cached context, offering a 90% discount over standard ingestion. However, community consensus and developer cost analyses note that DeepSeek continues to offer more aggressive caching margins overall. For agent pipelines running persistent multi-turn conversational histories with 95%+ cache hit ratios, DeepSeek can yield lower cumulative API bills despite identical headline rates.

For developers seeking ready-to-use alternative models for lightweight inference, deploying endpoints like DeepSeek-V4.1-Flash provides instant baseline measurements for production systems.

Context Capacity and Input Handling

MiniMax M3.1 Flash expands beyond pure text workloads by supporting multimodal inputs natively inside a 1,000,000-token context window. Official documentation verifies:

• Context Window: 1,000,000 tokens (approximately 4.9 times larger than MiniMax's previous M2.5/M2.7 architectures).
• Input Types: Native ingestion of text, image files up to 10MB, and video uploads up to 512MB.
• Output Capabilities: Generates structured text and code; does not provide native raster image generation output.

DeepSeek V4.1 implementations typically emphasize high-efficiency text and code parsing, leaving MiniMax M3.1 Flash with an architectural advantage for teams auditing long-horizon documents paired with video or image attachments in a single context container.

Architecture and Reasoning Control Mechanics

MiniMax M3.1 Flash implements Tunable Thinking Depth across five discrete settings: low, medium, high, xhigh, and max. In API deployments, omitting the parameter automatically defaults execution to max.

Critically, reasoning cannot be bypassed. Official technical summaries confirm that passing thinking.type="disabled" causes the API endpoint to immediately throw an HTTP 400 response (Error 2013). This forces every generation to allocate tokens toward intermediate logic chains. Unofficial community documentation suggests the underlying architecture relies on a Mixture-of-Experts (MoE) design with approximately 428 billion total parameters and 23 billion active parameters per token, combined with sparse attention layers.

Which One Should You Use?

Choosing between MiniMax M3.1 Flash and DeepSeek V4.1 Flash requires balancing context requirements, structural speed, and the operational demands of your agents.

Choose MiniMax M3.1 Flash if:

• Your workflow ingests multimodal context: You need to feed video clips (up to 512MB) or image files alongside sprawling text documentation within a unified 1M-token boundary.
• Your pipeline executes tool-heavy agent automation: Your workloads target enterprise workflows, office automation benchmarks, and isolated single-turn procedural code builds where MiniMax exhibits restrained token bloat.
• You rely on native API routing: You utilize MiniMax ecosystem tools (such as MiniMax Code or Agent chat) where legacy M2.5/M2.7 traffic has transitioned automatically to M3.1 Flash.

Choose DeepSeek V4.1 Flash if:

• Low latency is your top constraint: Your application serves live interactive chats, autocompletions, or instant UI agents where 200+ tokens per second output velocity is non-negotiable.
• Prompt caching dominates your bills: You deploy continuous agent loops that read repeatedly from static system instructions and codebase snapshots, benefiting from DeepSeek's rock-bottom cache costs.
• You need higher general reasoning resilience: You require higher composite accuracy on diverse logic benchmarks and want to minimize hallucination rates on open-domain factual queries.

Frequently Asked Questions

Is MiniMax M3.1 Flash better than DeepSeek V4.1?

MiniMax M3.1 Flash outperforms DeepSeek V4.1 on select office automation and long-context evaluations like Automation-Bench, but DeepSeek V4.1 scores higher on broad reasoning indices like Artificial Analysis and frontend evaluations.

Is MiniMax M3.1 Flash cheaper than DeepSeek V4.1?

MiniMax M3.1 Flash lists identical base rates to DeepSeek V4.1 off-peak pricing at $0.15 per million input tokens and $0.60 per million output tokens, but DeepSeek provides cheaper prompt caching.

What is the context window difference between the two models?

MiniMax M3.1 Flash natively supports a 1,000,000-token context window with multimodal inputs, whereas DeepSeek V4.1 configurations vary by endpoint and provider.

Which model outputs tokens faster?

DeepSeek V4.1 Flash leads throughput at up to 209 tokens per second in benchmark runs, while MiniMax M3.1 Flash averages around 90 to 110 tokens per second.

Can you disable thinking mode in MiniMax M3.1 Flash?

No, MiniMax M3.1 Flash requires reasoning effort to remain enabled and rejects requests that attempt to disable thinking with an API 400 error.

When will model weights become available for local deployment?

MiniMax announced plans to release open weights for MiniMax M3.1 Flash on October 23, 2026, while quantized community variations of DeepSeek V4.1 already circulate across local deployment frameworks.

What to Watch Next

Track the scheduled October 23, 2026 open-weight release of MiniMax M3.1 Flash to confirm exact parameter sizes and local quantization viability on hardware like DGX Spark. Monitor community hallucination audits on long-horizon agent runs, and observe whether DeepSeek updates peak vs. off-peak pricing structures in response to MiniMax's aggressive base tiers.

Try related models on Kie.ai: DeepSeek-V4.1-Flash, Claude Haiku 5.5 and Gemini 4 Argon.