Intelligence
N/A
N/A
General reasoning and benchmark headroom.
UnavailableMiniMax: MiniMax M2-her is a budget-priced text-first model from MiniMax with partial runtime data, standard context posture, and the clearest fit around long-context research.
Benchmark blend
Dev workflow signal
Standard
Budget tier
MiniMax: MiniMax M2-her currently reads as a budget text-first option with standard context and a partially published runtime profile.
Decision Strip
Core buy-side signals stay in one pass. The rest of the page expands only after intelligence, speed, context, and price are clear.
General reasoning and benchmark headroom.
UnavailableLatency data is partial.
UnavailableHow much prompt and task state can stay in view.
Competitive$1.20 output / 1M
EfficientEditorial Profile
Positioning, tradeoffs, and fit are consolidated into one read instead of repeating the same story across separate cards.
MiniMax M2-her is a dialogue-first large language model built for immersive roleplay, character-driven chat, and expressive multi-turn conversations. Designed to stay consistent in tone and personality, it supports rich message...
MiniMax text-first profile
Long-context research with standard context and partially published runtime.
Efficient spend profile. More comfortable for sustained prompt volume if the capability fit is right.
The available source data suggests a balanced profile rather than one dominant edge.
Budget-friendly input pricing is a strength, but raw capability may vary by workload.
Latency data is incomplete, so interactive responsiveness is harder to rank confidently.
Current metadata points to a text-first profile rather than a broad multimodal one.
Context window is more comfortable for focused tasks than extremely long sessions.
Focused chat, retrieval-augmented flows, and narrower production tasks.
Benchmarks
Only benchmark categories with actual signal are shown. Secondary values stay as simple definitions instead of nested micro-cards.
Specs & Pricing
Specs stay neutral, pricing gets emphasis through values rather than extra containers. Raw provider internals remain in metadata at the end.
This model is relatively efficient on price. It is the easier fit when sustained prompt volume matters.
Metadata
Verification details remain available, but the page no longer forces them ahead of the editorial read.