MiniMax M2 pricing for resume parsing

Quick start

Call MiniMax M2 with the OpenAI Python SDK pointed at api.minimax.chat:

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.minimax.chat/v1",
    api_key=os.environ["MINIMAX_API_KEY"],
)
response = client.chat.completions.create(
    model="minimax-m2",
    max_tokens=800,
    messages=[{"role": "user", "content": "Your prompt here"}]
)
# Cost: ~$0.001450 per call at standard rates
# Input:  $0.26/MTok
# Output: $1.00/MTok

How MiniMax M2 compares on cost

Per-call cost for all models scoring 50+ on resume parsing benchmarks. Hover for details.

Highest-quality affordable option: Gemini 3 Flash at $0.003650/call (2.5x the cost, 86/100 quality). MiniMax M2 is already the cheapest choice for resume parsing.

What drives resume parsing cost

Resumes are longer than invoices and less uniformly structured, so input runs higher while output stays compact. Like invoice parsing this is a bulk, latency-tolerant workload where the batch API discount applies to the entire request. The parsing instructions are identical across every candidate, which puts them squarely in the cached prefix.

For MiniMax M2 specifically, that means output tokens cost 3.8x more than input, at $1.00 vs $0.26 per MTok. At this use case’s 2,500/800 token split, output accounts for 55% of the $0.001450 per-call cost.

MiniMax M2 quality benchmarks for resume parsing

Benchmark data for resume parsing is pending for MiniMax M2. Test against your own evaluation suite before moving to production.

Benchmark data is not yet available for MiniMax M2. We recommend testing against your own evaluation criteria.

About MiniMax M2

Prices from platform.minimax.io/docs/pricing/overview (read 2026-10-11). No cache discount published (set 1.0 — no discount). No batch discount published. Not yet benchmarked, so never recommended.

Other models from MiniMax

You can compare MiniMax M2 head-to-head against any other model on our model comparison pages, or track how pricing has changed over time on the pricing history tracker.

Frequently asked questions