NVIDIA Nemotron 3 Ultra vs Qwen 3.8 27B
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 27B holds the verified advantage (-3.8% difference). Qwen 3.8 27B is 2.5x cheaper on input tokens ($0.2/M vs $0.5/M). In Swarm multi-agent clusters, Both models sit in Tier 2 (Tier 2: Accelerated Systems Specialist). Excellent peer redundancy or parallel Coder swarm execution.
NVIDIA Nemotron 3 Ultra
Coder SubagentQwen 3.8 27B
Coder SubagentStandardized Coding & Reasoning Benchmarks
Verified, contamination-free benchmark scores with direct mathematical deltas.
| Benchmark Metric | NVIDIA Nemotron 3 Ultra | Qwen 3.8 27B | Performance Delta |
|---|---|---|---|
| SWE-bench VerifiedReal-world GitHub issues resolved autonomously | 58.2% | 62% | Qwen 3.8 27B (-3.8% difference) |
| LiveCodeBenchPost-training cutoff algorithmic challenges | 53% | 54.5% | Qwen 3.8 27B (-1.5% difference) |
| LMSYS Coding EloCrowdsourced blind developer preference | 1365 | 1342 | NVIDIA Nemotron 3 Ultra (+23Elo advantage) |
| AIME (Competition Math)Deep multi-step deductive reasoning | 77% | — | NVIDIA Nemotron 3 Ultra (Only measured on 77) |
Token Economics & Operational Cost Ratio
Pricing per 1M tokens and prompt caching advantages for long-horizon sessions.
Qwen 3.8 27B is 2.5x cheaper on input tokens ($0.2/M vs $0.5/M).
Output generation costs differ by 1.88x.
Prompt caching significantly reduces long-horizon AST parsing costs during multi-turn Swarm pairing sessions.
Swarm Multi-Agent Topology Synergy
How Swarm orchestrates NVIDIA Nemotron 3 Ultra and Qwen 3.8 27B in autonomous engineering clusters.
NVIDIA Nemotron 3 Ultra
Recommended: Coder Subagent
High code synthesis fidelity for performance-critical systems code and numerical computing.
Qwen 3.8 27B
Recommended: Coder Subagent
Executes air-gapped code changes locally inside worktrees.
When to Choose Which Model
Practical developer recommendations based on real-world engineering workloads.
Choose NVIDIA Nemotron 3 Ultra if:
- ✓Superb inference efficiency and throughput on NVIDIA hardware
- ✓Strong numerical computing, CUDA, and systems programming comprehension
- ✓High mathematical reasoning scores (77.0% AIME)
- ✓Need Coder Subagent specialization inside Swarm.
Choose Qwen 3.8 27B if:
- ✓Runs locally on consumer hardware while delivering 34 Intelligence Index
- ✓110 TPS throughput with negligible token cost ($0.18/task)
- ✓Strong multilingual and code repair abilities
- ✓Need Coder Subagent specialization inside Swarm.
Frequently Asked Comparison Questions
Which model is better for autonomous software engineering: NVIDIA Nemotron 3 Ultra or Qwen 3.8 27B?
For autonomous agentic coding where multi-file patch pass rate is paramount, Qwen 3.8 27B holds the verified advantage (-3.8% difference). Qwen 3.8 27B is 2.5x cheaper on input tokens ($0.2/M vs $0.5/M). In Swarm multi-agent clusters, Both models sit in Tier 2 (Tier 2: Accelerated Systems Specialist). Excellent peer redundancy or parallel Coder swarm execution. On SWE-bench Verified, NVIDIA Nemotron 3 Ultra scores 58.2% vs Qwen 3.8 27B at 62%.
How do NVIDIA Nemotron 3 Ultra and Qwen 3.8 27B compare on token pricing and operational economics?
Qwen 3.8 27B is 2.5x cheaper on input tokens ($0.2/M vs $0.5/M). On cached prompts, NVIDIA Nemotron 3 Ultra costs $0.15/M vs Qwen 3.8 27B at $0.04/M.
How does Swarm configure NVIDIA Nemotron 3 Ultra and Qwen 3.8 27B in multi-agent workflows?
Both models sit in Tier 2 (Tier 2: Accelerated Systems 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.