DeepSeek V4.1 Flash vs MiniMax M3.1 Flash

At a glance

DeepSeek V4.1 Flash and MiniMax M3.1 Flash are both low-cost models built for AI agents, coding automation, and multi-step tasks. The primary distinction is speed versus benchmark coverage: MiniMax prioritizes throughput and agentic task cost reduction, while DeepSeek emphasizes benchmark performance in the latest 1017 update. Both support multimodal inputs and cost substantially less than frontier models.

Aspect	DeepSeek V4.1 Flash	MiniMax M3.1 Flash
Maker	DeepSeek	MiniMax
Release	September 10, 2026 (Flash); October 17, 2026 (Flash-1017)	October 23, 2026
Access	API, web app	API, M Plan, MiniMax Code desktop app
Context window	1,048,576 tokens (1M)	Not published
Input pricing	$0.30/1M tokens (peak)	$0.15/1M tokens
Output pricing	$1.20/1M tokens (peak)	$0.60/1M tokens
Cached input	Not published	$0.015/1M tokens
Best for	Software engineering, complex reasoning, agent benchmarks	Speed-critical agents, multi-turn automation, cost optimization

DeepSeek V4.1 Flash: strengths and limits

DeepSeek released V4.1 Flash-1017 on October 17, 2026, with improvements across published benchmarks relative to the base V4.1 Flash model. The company reports scores of 42.6 on HLE, 77.0 on DeepSWE v1.1, 72.1 on Apex Bench v1.1, and 83.0 on Chartography with tools. For agent automation, it achieves 60.5 on Automation-Bench v1.0.6 and 35.4 on Agents’ Last Exam.

The model includes native multimodal visual understanding and supports a 1M token context window. According to DeepSeek, the 1017 update reduced task runtime and output length while maintaining performance, enabling lower cost and higher speed. The company optimized the model for DeepSeek Harness 0.2.1-rc.1 and third-party platforms including Tencent CodeBuddy and WorkBuddy.

Limitations include higher per-token pricing than MiniMax M3.1 Flash at peak rates. The vendor does not publish inference speed metrics, so throughput comparisons depend on third-party reports. DeepSeek does not disclose cached input pricing. The model is not available as open weights.

MiniMax M3.1 Flash: strengths and limits

MiniMax M3.1 Flash emphasizes speed and cost for agentic workflows. The company reports output speeds reaching 200+ tokens per second in typical use, with median P50 speeds at 170+ TPS through optimized sparsity. On benchmarks, it scores 73.7 percent on DeepSWE v1.1, 76.2 on BankerToolBench, 61.7 on OSWorld V2, and 82 percent on CyberGym.

For automation tasks, MiniMax states that M3.1 Flash ranks among top performers on AutomationBench-AA and is ranked first among Chinese models on OSWorld V2 and Agents’ Last Exam with 29.6 points. The company reports that agentic task usage cost is 70 percent lower than its predecessor, with flat pricing at $0.15 per 1M input tokens and $0.60 per 1M output tokens. Cached input pricing is $0.015 per 1M tokens.

Limitations include less detailed benchmark documentation in public materials compared to DeepSeek. The context window length is not disclosed. MiniMax states that model weights will be available later but are not currently released. Speed figures are reported by the vendor and third-party sources.

Price and access

DeepSeek V4.1 Flash-1017 costs $0.30 per 1M input tokens and $1.20 per 1M output tokens at peak rates. Access is provided through the DeepSeek API, web application, and official mobile apps. The context window is 1M tokens. Cached input pricing is not published.

MiniMax M3.1 Flash is priced at $0.15 per 1M input tokens and $0.60 per 1M output tokens, with cached input at $0.015 per 1M tokens. Access is available through the MiniMax API (platform.minimax.io), M Plan subscription, and the MiniMax Code desktop application. Context window is not published. Model weights will be released later.

At current pricing, MiniMax is approximately 50 percent cheaper on input and 50 percent cheaper on output tokens compared to DeepSeek’s peak rates. For workflows using extensive context caching, MiniMax’s published cached input rate provides additional cost reduction. This pricing advantage compounds for high-volume agentic deployments. Like many AI agent frameworks, both models benefit from prompt caching in production environments.

Which one should you choose

Choose DeepSeek V4.1 Flash-1017 if you need the highest published benchmark scores in software engineering and complex reasoning. It is suitable for teams building code agents, security automation, or workflows where reasoning quality outweighs per-token cost. The 1M context window accommodates large code files and extended conversation histories.

Choose MiniMax M3.1 Flash if you operate high-frequency agent loops or require maximum throughput. It fits customer service bots, sales automation, SaaS workflow automation, and scenarios where you pay per output token and speed reduces total execution time. The reported 70 percent cost reduction on agentic tasks makes it more economical for chatbot platforms and multi-turn automation. It is also preferable if you want publicly available cached input pricing for optimizing long-context reuse.

For budget-constrained teams, calculate total monthly cost using your expected token volumes and query frequency. If your agents run 100+ queries per day and cost is the priority, MiniMax typically offers lower total expenditure. If you run fewer, longer queries where reasoning depth matters, DeepSeek-1017 may justify the higher price. Both are substantially cheaper than frontier models for agent workloads.

Common questions

Is DeepSeek V4.1 Flash good for agents?

DeepSeek V4.1 Flash-1017 reports 60.5 on Automation-Bench v1.0.6 and 35.4 on Agents’ Last Exam. These benchmark scores indicate capability for agentic tasks. Whether it meets your requirements depends on the specific use case, domain, and expected complexity of tasks your agent will handle.

Why is MiniMax M3.1 Flash priced lower?

MiniMax reports using aggressive sparsity optimization to reduce inference cost and states that agentic task usage cost is 70 percent lower than its predecessor model. The company does not provide a detailed breakdown of its cost structure. Performance on benchmarks suggests the lower price reflects architectural efficiency rather than reduced capability.

What is the speed difference?

MiniMax reports output speeds of 200+ tokens per second with median P50 speeds at 170+ TPS. DeepSeek does not publish inference speed metrics. Third-party benchmarks show variation depending on testing conditions, so direct speed comparison requires testing both models on your specific workloads.

Which model has better context length?

DeepSeek V4.1 Flash supports 1M tokens. MiniMax M3.1 Flash context window is not published. For applications requiring extended context, DeepSeek’s disclosed 1M token window is a known advantage.

The conclusion

DeepSeek V4.1 Flash-1017 and MiniMax M3.1 Flash represent a new cost tier for capable models, exchanging frontier reasoning for speed and economy. Both support multimodal inputs and are optimized for production agent workflows. DeepSeek leads on published reasoning and coding benchmarks, while MiniMax delivers superior reported throughput and lower per-token pricing for high-volume automation.

The choice depends on three factors: benchmark confidence, cost predictability, and speed requirements. DeepSeek suits teams prioritizing reasoning performance assurance on complex tasks. MiniMax suits teams measuring success by tokens per second and total bill size. Both provide production APIs and transparent pricing as practical alternatives to expensive frontier models.

When evaluating either model for your workflows, test both with samples of your actual prompts and measure latency and output quality. Published benchmarks provide useful context but do not always predict agent behavior on your specific tasks. Consider using frameworks that support both models to compare performance before committing to production infrastructure.