HEAD-TO-HEAD BENCHMARK COMPARISONTier 2 vs Tier 2

Inkling vs MiniMax-M3

Empirical evaluation across autonomous coding benchmarks (SWE-bench Verified, LiveCodeBench), token economics, and multi-agent topology in Swarm.

TECHNICAL VERDICT

For autonomous agentic coding where multi-file patch pass rate is paramount, MiniMax-M3 holds the verified advantage (-3.5% difference). Inkling is 1.67x cheaper on input tokens ($0.15/M vs $0.25/M). In Swarm multi-agent clusters, Both models sit in Tier 2 (Tier 2: Code Search & Discovery Specialist). Excellent peer redundancy or parallel Coder swarm execution.

Inkling

Finder Subagent
SWE-bench Verified:54.5%
LiveCodeBench:47%
Pricing (In / Out):$0.15 / $0.6
VS

MiniMax-M3

Coder Subagent
SWE-bench Verified:58%
LiveCodeBench:50.5%
Pricing (In / Out):$0.25 / $1

Standardized Coding & Reasoning Benchmarks

Verified, contamination-free benchmark scores with direct mathematical deltas.

Benchmark MetricInklingMiniMax-M3Performance Delta
SWE-bench VerifiedReal-world GitHub issues resolved autonomously54.5%58%MiniMax-M3 (-3.5% difference)
LiveCodeBenchPost-training cutoff algorithmic challenges47%50.5%MiniMax-M3 (-3.5% difference)
LMSYS Coding EloCrowdsourced blind developer preference13051320MiniMax-M3 (-15Elo difference)
AIME (Competition Math)Deep multi-step deductive reasoning——Not published for both models

Token Economics & Operational Cost Ratio

Pricing per 1M tokens and prompt caching advantages for long-horizon sessions.

Prompt Input Tokens
Inkling$0.15 / 1M
MiniMax-M3$0.25 / 1M

Inkling is 1.67x cheaper on input tokens ($0.15/M vs $0.25/M).

Completion Output Tokens
Inkling$0.6 / 1M
MiniMax-M3$1 / 1M

Output generation costs differ by 1.67x.

Context Caching Advantage
Inkling$$0.03 / 1M
MiniMax-M3$$0.05 / 1M

Prompt caching significantly reduces long-horizon AST parsing costs during multi-turn Swarm pairing sessions.

Swarm Multi-Agent Topology Synergy

How Swarm orchestrates Inkling and MiniMax-M3 in autonomous engineering clusters.

⚡

Optimal Cluster Configuration

Both models sit in Tier 2 (Tier 2: Code Search & Discovery Specialist). Excellent peer redundancy or parallel Coder swarm execution.

Tier 2

Inkling

Rapidly maps file paths and dependency graphs for the lead planner.

Worktree Isolation: Operates read-only across whole worktrees with zero risk of dirty state.
Tier 2

MiniMax-M3

Generates comprehensive documentation and test scaffolding quickly.

Worktree Isolation: Works in isolated docs/tests branches without impacting core logic.

When to Choose Which Model

Practical developer recommendations based on real-world engineering workloads.

Choose Inkling if:

  • ✓Fast 95 TPS generation at low cost ($0.15/task)
  • ✓Efficient grep and file discovery summaries
  • ✓Clean concise answers without unnecessary prose
  • ✓Need Finder Subagent specialization inside Swarm.

Choose MiniMax-M3 if:

  • ✓Inexpensive $0.25 cost per task with 85 TPS throughput
  • ✓Clean documentation and natural language explanations
  • ✓Reliable API availability and low latency
  • ✓Need Coder Subagent specialization inside Swarm.

Frequently Asked Comparison Questions

Which model is better for autonomous software engineering: Inkling or MiniMax-M3?

For autonomous agentic coding where multi-file patch pass rate is paramount, MiniMax-M3 holds the verified advantage (-3.5% difference). Inkling is 1.67x cheaper on input tokens ($0.15/M vs $0.25/M). In Swarm multi-agent clusters, Both models sit in Tier 2 (Tier 2: Code Search & Discovery Specialist). Excellent peer redundancy or parallel Coder swarm execution. On SWE-bench Verified, Inkling scores 54.5% vs MiniMax-M3 at 58%.

How do Inkling and MiniMax-M3 compare on token pricing and operational economics?

Inkling is 1.67x cheaper on input tokens ($0.15/M vs $0.25/M). On cached prompts, Inkling costs $0.03/M vs MiniMax-M3 at $0.05/M.

How does Swarm configure Inkling and MiniMax-M3 in multi-agent workflows?

Both models sit in Tier 2 (Tier 2: Code Search & Discovery Specialist). Excellent peer redundancy or parallel Coder swarm execution. Swarm isolates code modifications in independent Git worktrees, allowing both models to operate in parallel without colliding working directories or git branch conflicts.

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