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DeepSeek V4 Pro vs MiniMax M3

MiniMax M3 has been retired from our catalogue. Its figures below are the last published rates; calls to it are no longer served, while the other model in this comparison is.

vs

Which one, when

Both hold a 1000000-token context, cover chat, code, reasoning and tools, and let thinking be disabled, so price and inputs decide it. minimax-m3 is cheaper on every published rate - $0.3 against $1.32 per million input, $1.2 against $3.96 on output, $0.06 against $0.132 on cache reads - takes image and video alongside text, and allows 524288 output tokens against 393216. It has left our catalogue, so deepseek-v4-pro is the callable one of the two; pick it for text-only work at these rates.

Benchmarks

MiniMax M3: the vendor has not published benchmark scores.

Above averageDeepSeek V4 Pro5 / 10
DeepSeek V4 Pro MiniMax M3 other models measured peer average ★ no peer scored higher
SWE-Bench Pro
59%
N/A
CoWorkBench max
66.3%
N/A
Humanity's Last Exam no tools
37.7%
N/A
MCP-Mark
57.1%
N/A

Vendor-published: Alibaba (Qwen) Anthropic DeepSeek Google MiniMax Moonshot OpenAI Tencent Z.ai

Pricing

DeepSeek V4 Pro MiniMax M3 Δ
Input / 1M tokens $1.32 $0.3 4.4×
Output / 1M tokens $3.96 $1.2 3.3×
Cache read / 1M tokens $0.132 $0.06 2.2×
Cache write no separate charge no separate charge -

Rates from the live catalogue at build time; each model page carries the current rate card.

Where they sit · input price per 1M tokens across all 76 chat models on this billing unit (log scale)

DeepSeek V4 Pro · $1.32 MiniMax M3 · $0.3
$0.05 · Qwen3 VL Flash $30 · GPT-5.4 Pro

Capabilities

DeepSeek V4 Pro MiniMax M3
Tool calling yes yes
Thinking control configurable configurable
Structured output yes -
Prompt caching implicit (automatic) implicit (automatic)
Cache lifetime no fixed TTL (evicted when unused) not published
Minimum cached prefix not published 512 tokens

Specs

DeepSeek V4 Pro MiniMax M3
Input modalities text text image video
Output modalities text text
Released 2026-04-24 2026-06-01
Context window 1M 1M
Max output 393K 524K
Thinking parameter
  • thinking.type
  • reasoning_effort
  • thinking.type
  • reasoning_split
Accepted values
thinking.type
  • enabled
  • disabled
reasoning_effort
  • high
  • max (low and medium map to high, xhigh maps to max)
thinking.type
  • adaptive
  • disabled
reasoning_split
  • boolean
Default

enabled, with reasoning_effort high

some complex agent requests are automatically set to max

adaptive: thinking on, with the model deciding when extra reasoning helps

Specs are transcribed from each vendor’s documentation; a row a vendor does not publish is left out rather than inferred. Full sources: DeepSeek V4 Pro · MiniMax M3

Switch between them with one line

Both ids are in every tab below; the highlighted pair of lines is the only edit. Same endpoint, same key, same request shape.

from openai import OpenAI

client = OpenAI(
    base_url="https://synthorai.io/v1",
    api_key="sk-syn-...",
)

resp = client.chat.completions.create(
    model="deepseek-v4-pro",
    # model="minimax-m3",  # uncomment this line, comment the one above
    messages=[{"role": "user", "content": "Summarize this diff"}],
    reasoning_effort="medium",
)
print(resp.choices[0].message.content)

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FAQ

Which is cheaper, DeepSeek V4 Pro or MiniMax M3?

MiniMax M3 is cheaper on the "Input / 1M tokens" row ($0.3 vs $1.32, 4.4× apart). Other rows may point the other way; the table above carries the full rate card, and real cost depends on your mix.

Can I A/B test DeepSeek V4 Pro against MiniMax M3 without two integrations?

Yes. Both are served through the same OpenAI-compatible endpoint with one API key. Switching is a one-line change to the model id, so you can route a fraction of traffic to each and compare bills directly.

Do DeepSeek V4 Pro and MiniMax M3 support prompt caching?

Yes. Both bill cache reads below their input rate, so warm-prefix workloads cost less than the list rates suggest. The exact cache-read rows are in the pricing table above.

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