LLM Comparison

SWE-1.7 vs MiniMax-M3: benchmark scores, pricing & comparison.

Side-by-side SWE-1.7 vs MiniMax-M3 comparison across SWE-bench, GPQA, HLE, Terminal-Bench, coding agent scores, token pricing, context window, and AskClash RWT. Green marks the winner on each benchmark.

Open live leaderboardSWE-1.7 model pageMiniMax-M3 model page
Rank #30 vs #43AskClash overall scores 51.1 vs 37.9.
Pricing $0/$0 vs $0.30/$1.20Input and output token prices per 1M tokens when published.
Proprietary vs Open WeightCognition vs MiniMax.

SWE-1.7 vs MiniMax-M3 benchmark comparison

Green cells highlight the winning model for each metric. Scores are cached from the AskClash LLM leaderboard snapshot.

MetricSWE-1.7MiniMax-M3
Overall Score51.137.9
Leaderboard Rank#30#43
ACB—29.8
RWT8.58.5
HLE—37.1
GPQA—92.9
IFEval—82.9
SWE-bench—80.5
SWE-Pro—59.0
Terminal-Bench81.566.0
GDPval-AA—58.4
OSWorld—70.1
MCP Atlas—74.2
Finance Agent—48.3
MMMU-Pro—78.1
Tau2—88.9
Input Price (per 1M tokens)$0$0.30
Output Price (per 1M tokens)$0$1.20
Context Window256K1M
Benchmarks Published114

SWE-1.7 vs MiniMax-M3 head-to-head charts

SWE-1.7 leads 2 and MiniMax-M3 leads 0 of 3 shared benchmarks. SWE-1.7 is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.

SWE-1.7MiniMax-M3
Overall
51.1SWE-1.7
37.9MiniMax-M3
RWT
8.5SWE-1.7
8.5MiniMax-M3
Terminal-Bench
81.5SWE-1.7
66.0MiniMax-M3
SWE-1.7
Input$0
Output$0
Workload$0.00
Context256K
MiniMax-M3
Input$0.30
Output$1.20
Workload$0.54
Context1M

Workload = published cost of 1M input + 200K output tokens. Open the live leaderboard for interactive compare charts.

More SWE-1.7 and MiniMax-M3 comparisons

Explore how SWE-1.7 and MiniMax-M3 stack up against other top-ranked LLMs.

Claude Fable 5.1 vs SWE-1.7Claude Fable 5.1 vs MiniMax-M3Claude Fable 5 vs SWE-1.7Claude Fable 5 vs MiniMax-M3GPT-5.6 Sol vs SWE-1.7GPT-5.6 Sol vs MiniMax-M3Claude Opus 5 vs SWE-1.7Claude Opus 5 vs MiniMax-M3Grok 4.6 vs SWE-1.7Grok 4.6 vs MiniMax-M3

How to read this comparison

Benchmark scores

Higher is better for all benchmark scores (SWE-bench, GPQA, HLE, Terminal-Bench, etc.). Green marks the model with the higher score.

Token pricing

Lower is better for input and output prices. Green marks the cheaper model per 1M tokens.

Coverage matters

Models with fewer disclosed benchmark cells may have inflated percentile scores. Check the benchmark cell count for context.

This comparison page is generated from the AskClash LLM leaderboard cache. Open the live leaderboard for real-time scores and interactive filtering.