Kimi K3 vs Qwen 3.8 Max (0902)
Empirical evaluation across autonomous coding benchmarks (SWE-bench Verified, LiveCodeBench), token economics, and multi-agent topology in Swarm.
For autonomous agentic coding where multi-file patch pass rate is paramount, Qwen 3.8 Max (0902) holds the verified advantage (-2.6% difference). Qwen 3.8 Max (0902) is 1.25x cheaper on input tokens ($0.8/M vs $1/M). In Swarm multi-agent clusters, Both models sit in Tier 1 (Tier 1: Long-Context Architect). Excellent peer redundancy or parallel Coder swarm execution.
Kimi K3
Primary / PlannerQwen 3.8 Max (0902)
Primary / PlannerStandardized Coding & Reasoning Benchmarks
Verified, contamination-free benchmark scores with direct mathematical deltas.
| Benchmark Metric | Kimi K3 | Qwen 3.8 Max (0902) | Performance Delta |
|---|---|---|---|
| SWE-bench VerifiedReal-world GitHub issues resolved autonomously | 70.2% | 72.8% | Qwen 3.8 Max (0902) (-2.6% difference) |
| LiveCodeBenchPost-training cutoff algorithmic challenges | 62% | 64% | Qwen 3.8 Max (0902) (-2% difference) |
| LMSYS Coding EloCrowdsourced blind developer preference | 1390 | 1402 | Qwen 3.8 Max (0902) (-12Elo 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.
Qwen 3.8 Max (0902) is 1.25x cheaper on input tokens ($0.8/M vs $1/M).
Output generation costs differ by 1.25x.
Prompt caching significantly reduces long-horizon AST parsing costs during multi-turn Swarm pairing sessions.
Swarm Multi-Agent Topology Synergy
How Swarm orchestrates Kimi K3 and Qwen 3.8 Max (0902) in autonomous engineering clusters.
Kimi K3
Recommended: Primary / Planner
Ingests large dependency graphs to construct accurate Task Program DAG stages.
Qwen 3.8 Max (0902)
Recommended: Primary / Planner
Reliable planner for enterprise polyglot repositories.
When to Choose Which Model
Practical developer recommendations based on real-world engineering workloads.
Choose Kimi K3 if:
- ✓Intelligence Index 44 with outstanding 47 Finance & Accounting score
- ✓Large 2M token context window for full-repo architectural planning
- ✓Cost-efficient $1.20 cost per task with high code synthesis precision
- ✓Need Primary / Planner specialization inside Swarm.
Choose Qwen 3.8 Max (0902) if:
- ✓Intelligence Index 45 with low $0.80 cost per task
- ✓Exceptional multilingual code and docstring comprehension
- ✓Clean diff generation with zero hallucinated package dependencies
- ✓Need Primary / Planner specialization inside Swarm.
Frequently Asked Comparison Questions
Which model is better for autonomous software engineering: Kimi K3 or Qwen 3.8 Max (0902)?
For autonomous agentic coding where multi-file patch pass rate is paramount, Qwen 3.8 Max (0902) holds the verified advantage (-2.6% difference). Qwen 3.8 Max (0902) is 1.25x cheaper on input tokens ($0.8/M vs $1/M). In Swarm multi-agent clusters, Both models sit in Tier 1 (Tier 1: Long-Context Architect). Excellent peer redundancy or parallel Coder swarm execution. On SWE-bench Verified, Kimi K3 scores 70.2% vs Qwen 3.8 Max (0902) at 72.8%.
How do Kimi K3 and Qwen 3.8 Max (0902) compare on token pricing and operational economics?
Qwen 3.8 Max (0902) is 1.25x cheaper on input tokens ($0.8/M vs $1/M). On cached prompts, Kimi K3 costs $0.15/M vs Qwen 3.8 Max (0902) at $0.15/M.
How does Swarm configure Kimi K3 and Qwen 3.8 Max (0902) in multi-agent workflows?
Both models sit in Tier 1 (Tier 1: Long-Context Architect). 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.