MiniMax M3 vs. DeepSeek V4 Pro: Pricing, Context, API, and Coding Performance

MiniMax M3 and DeepSeek V4 Pro are both designed for large-scale coding, reasoning, tool use, and AI-agent workloads. They are similar in ambition, but their pricing models and API behavior are different.

The most important difference is cost structure:

• MiniMax M3 costs $0.30 per million input tokens and $1.20 per million output tokens for requests up to 512K input tokens under the current standard rate.
• DeepSeek V4 Pro costs $0.66 per million input tokens during off-peak hours and $1.98 per million output tokens during off-peak hours.
• DeepSeek V4 Pro supports a 1M-token context window and up to 384K output tokens.
• MiniMax M3 supports a 1M-token context window and is available through both the MiniMax API and an Anthropic-compatible API.

The right choice depends on your input size, output length, cache behavior, traffic schedule, concurrency requirements, tool-calling workflow, and total cost per completed task.

Key Takeaways

Choose MiniMax M3 when you need:

• Lower standard-rate input and output pricing for prompts up to 512K tokens
• A 1M-token context window
• Native text, image, and video input through the M3 Anthropic-compatible interface
• Anthropic SDK compatibility
• Coding-agent and tool-use workflows
• Automatic prompt caching through the MiniMax API

Choose DeepSeek V4 Pro when you need:

• A 1M-token context window
• Up to 384K output tokens
• OpenAI-compatible and Anthropic-compatible API formats
• Clear peak and off-peak pricing
• High concurrency for large production workloads
• JSON output, tool calls, Responses API, and thinking mode

Neither model is universally better. The practical winner is the model that completes your real tasks with the lowest cost per successful result.

Official MiniMax M3 Pricing

MiniMax separates its API pricing into standard and priority service tiers. The current standard pricing page lists the following effective MiniMax M3 rates:

MiniMax M3 request type	Input price	Output price	Prompt-cache read
Input up to 512K tokens	$0.30 / 1M tokens	$1.20 / 1M tokens	$0.06 / 1M tokens
Input above 512K tokens	$0.60 / 1M tokens	$2.40 / 1M tokens	$0.12 / 1M tokens

The MiniMax pricing page displays the original $0.60/$2.40 and $1.20/$4.80 rates with a current permanent 50% reduction for the listed standard tier.

MiniMax also states that priority service is priced at 1.5 times the standard price. Priority service is intended for requests that need priority admission and faster processing.

The billing unit is the number of tokens consumed. MiniMax notes that the token-to-character ratio varies by usage scenario.

Read the official MiniMax pay-as-you-go pricing documentation for the active account terms.

MiniMax M3 Pricing Examples

For a request containing 100,000 input tokens and generating 20,000 output tokens within the 512K threshold:

• Input cost: 0.1 × $0.30 = $0.03
• Output cost: 0.02 × $1.20 = $0.024
• Estimated model usage cost: $0.054

For a request containing 600,000 input tokens and generating 20,000 output tokens:

• Input cost: 0.6 × $0.60 = $0.36
• Output cost: 0.02 × $2.40 = $0.048
• Estimated model usage cost: $0.408

The second request costs more because it crosses the 512K input threshold. The higher rate applies to the long-context pricing tier.

Official DeepSeek V4 Pro Pricing

DeepSeek’s official pricing page lists the model identifier deepseek-v4-pro and version DeepSeek-V4-Pro-0813.

DeepSeek charges per 1 million tokens and separates rates into peak and off-peak periods:

DeepSeek V4 Pro usage	Off-peak price	Peak price
Input tokens, cache hit	$0.022 / 1M tokens	$0.044 / 1M tokens
Input tokens, cache miss	$0.66 / 1M tokens	$1.32 / 1M tokens
Output tokens	$1.98 / 1M tokens	$3.96 / 1M tokens

DeepSeek defines peak hours as:

• 01:00–04:00 UTC, Monday through Friday
• 06:00–10:00 UTC, Monday through Friday

All other listed hours are off-peak.

DeepSeek states that the charge is calculated as:

Number of tokens × applicable price

Fees are deducted from the account balance according to the provider’s billing rules.

See the official DeepSeek V4 Pro pricing documentation.

DeepSeek V4 Pro Pricing Examples

For 100,000 cache-miss input tokens and 20,000 output tokens during off-peak hours:

• Input cost: 0.1 × $0.66 = $0.066
• Output cost: 0.02 × $1.98 = $0.0396
• Estimated model usage cost: $0.1056

For the same request during peak hours:

• Input cost: 0.1 × $1.32 = $0.132
• Output cost: 0.02 × $3.96 = $0.0792
• Estimated model usage cost: $0.2112

DeepSeek’s cache-hit rate is dramatically lower than its cache-miss rate. Workloads with stable repeated prefixes can therefore have very different costs from workloads that send mostly new context.

MiniMax M3 vs. DeepSeek V4 Pro Price Comparison

For requests under 512K input tokens, MiniMax M3’s current standard rate is:

• $0.30 per million input tokens
• $1.20 per million output tokens

DeepSeek V4 Pro’s off-peak cache-miss rate is:

• $0.66 per million input tokens
• $1.98 per million output tokens

Under these conditions, MiniMax M3 has a lower listed input and output rate.

However, DeepSeek V4 Pro can become substantially cheaper for repeated cached prompts:

• DeepSeek cache-hit input: $0.022 per million tokens off-peak
• MiniMax M3 cache-read input: $0.06 per million tokens

The comparison changes again during DeepSeek peak hours. A team that sends large batches during peak periods may pay twice as much as it would during off-peak periods.

Normalized Cost Example

Assume every task uses:

• 200,000 input tokens
• 20,000 output tokens
• No cache hit
• No retries
• No platform fees

MiniMax M3, within the 512K tier:

• Input: 0.2 × $0.30 = $0.06
• Output: 0.02 × $1.20 = $0.024
• Total: $0.084 per task

DeepSeek V4 Pro, off-peak:

• Input: 0.2 × $0.66 = $0.132
• Output: 0.02 × $1.98 = $0.0396
• Total: $0.1716 per task

DeepSeek V4 Pro, peak:

• Input: 0.2 × $1.32 = $0.264
• Output: 0.02 × $3.96 = $0.0792
• Total: $0.3432 per task

These figures cover model token usage only. They exclude retries, tool calls, storage, network transfer, and gateway fees.

Context Window and Maximum Output

Both models target very large-context workloads.

Capability	MiniMax M3	DeepSeek V4 Pro
Context window	Up to 1,000,000 tokens	1,000,000 tokens
Maximum output	Confirm in the active MiniMax endpoint	Up to 384K tokens
Coding and agent tasks	Officially supported	Officially supported
Tool calls	Supported	Supported
Thinking mode	Supported through the MiniMax API format	Thinking mode supported
JSON output	Check the selected MiniMax endpoint	Supported
Anthropic API format	Supported by MiniMax M3	Supported by DeepSeek V4 Pro
OpenAI API format	Supported through the standard MiniMax interface	Supported

A large context window is useful for:

• Large code repositories
• Long technical documents
• Multi-file debugging
• Long-running coding agents
• Research workflows
• Large tool results
• Extended conversations

Large context also increases the risk of unnecessary cost. Repeated logs, irrelevant files, and stale tool results can consume tokens without improving the answer.

MiniMax M3 Multimodal and API Capabilities

MiniMax’s official Anthropic-compatible API documentation lists MiniMax M3 with a 1,000,000-token context window.

For the Anthropic-compatible interface, MiniMax M3 supports:

• Text content
• Image content
• Video content
• Tool use
• Tool results
• Thinking blocks
• Streaming responses
• System prompts
• Tool definitions
• Tool-choice controls
• Metadata

For multi-turn tool conversations, MiniMax states that the complete assistant response, including thinking, text, and tool-use blocks, should be appended to the conversation history.

Read the MiniMax Anthropic API documentation for the supported request fields.

DeepSeek V4 Pro API Capabilities

DeepSeek’s official pricing page lists DeepSeek V4 Pro with:

• 1M-token context length
• Maximum output of 384K tokens
• Thinking mode
• JSON output
• Tool calls
• Responses API
• Anthropic API compatibility
• Chat Prefix Completion
• FIM Completion in non-thinking mode

This gives developers flexibility when migrating an existing OpenAI-compatible or Anthropic-compatible application.

Use the exact model identifier accepted by the API account rather than assuming that the marketing name and API name are identical.

Tool Calling and Coding-Agent Performance

For coding agents, token price is only one part of the decision. The model must also complete tasks reliably.

Evaluate both MiniMax M3 and DeepSeek V4 Pro on:

• Repository navigation
• File selection
• Tool-argument accuracy
• Multi-step planning
• Patch completeness
• Test repair
• Error recovery
• Context management
• Security-sensitive changes
• Human review time

A useful coding-agent task may include:

1. A bug with a failing unit test
2. A feature request involving multiple files
3. A failing integration test
4. A refactor with compatibility requirements
5. A tool-driven repository investigation
6. A task requiring long logs and source files

The most important production metric is:

Cost per accepted patch = total model and platform cost ÷ accepted patches

A model with cheaper tokens may become more expensive if it requires repeated repairs.

Prompt Caching and Repeated Context

Caching can materially change the MiniMax M3 vs. DeepSeek V4 Pro cost comparison.

MiniMax M3 Cache Reads

MiniMax lists the following M3 prompt-cache read prices:

• Up to 512K input tokens: $0.06 per million tokens
• Above 512K input tokens: $0.12 per million tokens

These prices apply to cached input reads under the listed standard tier.

DeepSeek V4 Pro Cache Reads

DeepSeek V4 Pro cache-hit input pricing is:

• $0.022 per million tokens off-peak
• $0.044 per million tokens peak

DeepSeek’s cache-hit rate is much lower than its cache-miss rate. Applications with stable system instructions, tool schemas, or repository prefixes may benefit significantly from cache reuse.

Which Model Is Cheaper With Caching?

For cached input alone, DeepSeek V4 Pro’s listed cache-hit rate is lower than MiniMax M3’s cache-read rate.

However, total cost still depends on:

• Cache hit rate
• Output tokens
• Peak or off-peak schedule
• Prompt length
• Tool calls
• Retries
• Task success
• Context threshold

A model that appears cheaper for cached input may still cost more if it produces longer outputs or needs more repair cycles.

Peak and Off-Peak Scheduling

DeepSeek V4 Pro has explicit peak and off-peak rates. This creates a scheduling opportunity for batch workloads.

Suitable off-peak jobs include:

• Repository indexing
• Offline document analysis
• Evaluation runs
• Dataset transformation
• Batch summarization
• Non-interactive coding tasks

Interactive workloads cannot always wait for off-peak hours. For real-time applications, include peak pricing in the budget.

MiniMax’s published standard M3 table does not use the same peak/off-peak schedule. Instead, it separates standard and priority admission tiers.

This means the two pricing systems optimize for different operational choices:

• DeepSeek: time-based pricing
• MiniMax: context-tier and service-tier pricing

Concurrency, Rate Limits, and Throughput

DeepSeek’s official pricing page lists a concurrency limit of 500 for DeepSeek V4 Pro.

The same table lists a context length of 1M and a maximum output of 384K.

MiniMax’s exact rate limits depend on the account, endpoint, and service tier. The Anthropic-compatible interface supports a defined set of models and parameters, but production concurrency should be confirmed in the active MiniMax account documentation.

Throughput depends on:

• Concurrent requests
• Input length
• Output length
• Queue depth
• Region
• Streaming mode
• Tool calls
• Provider load
• Retry behavior

A high concurrency limit does not guarantee low latency. Track time to first token, total completion time, queue delay, and error rate.

OpenAI-Compatible vs. Anthropic-Compatible Integration

MiniMax M3 and DeepSeek V4 Pro can both be used in Anthropic-compatible workflows, but implementation details still differ.

MiniMax M3 Anthropic Integration

MiniMax provides:

ANTHROPIC_BASE_URL=https://api.minimax.io/anthropic

The Anthropic-compatible MiniMax interface supports M3 and exposes model-specific fields such as thinking, tools, tool choice, streaming, and multimodal content.

DeepSeek V4 Pro Anthropic Integration

DeepSeek provides:

ANTHROPIC_BASE_URL=https://api.minimax.io/anthropic

DeepSeek’s documentation lists Anthropic API compatibility as a supported feature for DeepSeek V4 Pro.

Why Compatibility Does Not Mean Identical Behavior

Even when two providers use the same SDK style, they may differ in:

• Tokenization
• Context limits
• Tool schemas
• Thinking behavior
• Streaming events
• Error codes
• Rate limits
• Usage fields
• Retry behavior

Keep the application’s internal request format separate from the final provider payload.

Production Cost Model for MiniMax M3 and DeepSeek V4 Pro

A realistic cost model includes more than input and output rates.

Use:

Total task cost = input + output + cached input + retries + tool calls + storage + transfer + review

For an AI coding agent, also track:

• Number of model calls
• Number of tool calls
• Failed tool executions
• Patch attempts
• Test-repair cycles
• Human corrections
• Time to completion
• Accepted-task rate

Example: 1,000 Coding Tasks

Assume each task uses:

• 200,000 input tokens
• 20,000 output tokens
• No cache hits
• One successful completion
• No platform fees

MiniMax M3 within the 512K tier:

• $0.084 per task
• $84 for 1,000 tasks

DeepSeek V4 Pro off-peak:

• $0.1716 per task
• $171.60 for 1,000 tasks

DeepSeek V4 Pro peak:

• $0.3432 per task
• $343.20 for 1,000 tasks

If MiniMax M3 requires twice as many retries to produce an accepted patch, its practical cost advantage may disappear. This is why token prices should always be paired with success rate and repair cost.

Which Model Is Better for Long-Context Coding?

MiniMax M3 is a strong candidate when:

• Your input is usually below 512K tokens
• You want lower standard input and output pricing
• You need multimodal content in the Anthropic-compatible interface
• Your application uses Anthropic SDK patterns
• Automatic caching fits your workload
• You need a 1M-token context window

DeepSeek V4 Pro is a strong candidate when:

• You need up to 384K output tokens
• You want explicit peak and off-peak pricing
• Your workload benefits from cache-hit economics
• You need a 1M-token context window
• You want both OpenAI and Anthropic API formats
• Your production system needs a documented concurrency limit of 500

Which Model Is Better for AI Agents?

For agentic systems, compare:

• Tool-call validity
• Multi-step reasoning
• Context retention
• Error recovery
• Streaming stability
• Retry frequency
• Output length
• Cost per completed task

MiniMax M3’s official documentation highlights coding, agentic reasoning, tool use, and long-context tasks. DeepSeek V4 Pro’s official model table lists thinking mode, tool calls, JSON output, and Responses API support.

Neither feature list proves that one model will perform better on your specific agent. The final choice depends on your tools, prompts, repository, evaluation set, and operating region.

Using OctopusX AI for MiniMax M3 and DeepSeek V4 Pro

Applications that need several providers can place a model gateway between business logic and vendor APIs.

A gateway such as OctopusX AI may be evaluated for:

• Unified authentication
• Model selection
• Provider routing
• Shared task IDs
• Usage aggregation
• Fallback policies
• Cost reporting
• Latency monitoring
• Provider-specific adapters

The internal request can preserve common fields such as:

• Model
• Messages
• Tools
• Temperature
• Maximum output
• Streaming
• Request ID
• Project metadata

The gateway should still expose model-specific differences, including MiniMax’s 512K pricing threshold and DeepSeek’s peak/off-peak schedule. Review the OctopusX AI documentation and pricing page for currently supported integrations.

MiniMax M3 vs. DeepSeek V4 Pro Decision Matrix

Requirement	Better starting point	Reason
Lowest listed standard cost below 512K input	MiniMax M3	$0.30 input and $1.20 output per million tokens
Lowest cached-input price	DeepSeek V4 Pro	$0.022 per million cache-hit tokens off-peak
1M-token context	Both	Both official pages list 1M context support
Maximum output length	DeepSeek V4 Pro	Official page lists up to 384K output tokens
Native image and video input through Anthropic format	MiniMax M3	MiniMax documents image and video content blocks for M3
Explicit peak/off-peak scheduling	DeepSeek V4 Pro	Official pricing defines both schedules
Anthropic SDK integration	Both	Both provide Anthropic-compatible endpoints
OpenAI-compatible integration	DeepSeek V4 Pro and selected MiniMax interfaces	Verify the exact endpoint and request schema
Large-scale concurrency	DeepSeek V4 Pro	Official pricing table lists a concurrency limit of 500
Cost-sensitive coding agents	Start with MiniMax M3	Lower listed standard rates below the 512K threshold
Batch processing during off-peak hours	DeepSeek V4 Pro	Off-peak pricing can reduce input and output cost
Multi-provider routing	A documented gateway	Useful when switching models by task, cost, or availability

FAQs

Which Is Better, MiniMax M3 or DeepSeek V4?

Neither model is universally better. MiniMax M3 is the stronger choice for lower standard pricing on requests up to 512K input tokens, native image and video input through its Anthropic-compatible API, and automatic caching. DeepSeek V4 Pro is stronger when you need up to 384K output tokens, explicit peak and off-peak pricing, a documented concurrency limit of 500, and both OpenAI-compatible and Anthropic-compatible APIs.

For most cost-sensitive coding tasks below 512K input tokens, MiniMax M3 is the better starting point. For long-output generation, large-scale batch processing, or cache-heavy workloads, DeepSeek V4 Pro may be more suitable.

How Does MiniMax Compare to DeepSeek?

MiniMax M3 and DeepSeek V4 Pro both offer a 1M-token context window, tool calling, thinking capabilities, JSON output support, and Anthropic-compatible API access.

Their main differences are pricing and API behavior:

MiniMax M3: $0.30 per million input tokens and $1.20 per million output tokens for requests up to 512K input tokens.

DeepSeek V4 Pro: $0.66 per million cache-miss input tokens and $1.98 per million output tokens during off-peak hours.

DeepSeek V4 Pro cache-hit input costs $0.022 per million tokens during off-peak hours.

MiniMax M3 supports native text, image, and video content blocks through its Anthropic-compatible endpoint.

DeepSeek V4 Pro supports up to 384K output tokens and lists a concurrency limit of 500.

MiniMax is generally more attractive for standard sub-512K requests, while DeepSeek becomes more competitive for cache-heavy, off-peak, high-concurrency, or very long-output workloads.

Is MiniMax M3 Better Than Claude?

MiniMax M3 may be better for applications that need a 1M-token context window, native multimodal input, lower standard token rates, automatic caching, and an Anthropic-compatible API.

Claude may be better for teams already using Anthropic’s native Messages API, tools, SDKs, and production workflows.

The choice also depends on the Claude model:

Haiku 4.5: $1 input / $5 output per million tokens

Sonnet 5: $2 input / $10 output per million tokens

Opus 5: $5 input / $25 output per million tokens

MiniMax M3 is usually the more cost-focused option for large coding and agent workloads, while Claude can be the better operational choice when Anthropic-native integration and model-specific behavior are more important.

How Good Is MiniMax M3?

MiniMax M3 is designed for coding, agentic reasoning, tool use, long-context tasks, and native multimodal workflows. The official MiniMax documentation lists a 1M-token context window and support for text, image, video, tool-use, tool-result, thinking, streaming, and system-prompt content through its Anthropic-compatible API.

Its current standard pricing is:

$0.30 per million input tokens for requests up to 512K input tokens

$1.20 per million output tokens for requests up to 512K input tokens

$0.60 per million input tokens above the 512K threshold

$2.40 per million output tokens above the 512K threshold

$0.06 or $0.12 per million cached input tokens, depending on context tier

MiniMax M3 is a strong fit for large repositories, coding agents, multimodal analysis, and applications that need to control token costs. Its real performance should be judged by task success rate, tool-call accuracy, retry frequency, latency, and cost per completed task, not by token price alone.