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Results: Claude Code harness (swe2)

Benchmark results for every model run under the Claude Code coding agent with the swe2 skill on mcp-gateway-registry, generated from the committed run-summary.json files. Regenerate with uv run scripts/gen_agent_report.py --harness claude-code --skill swe2. Companion to the cross-harness comparison agentic-coding-swe-comparison-swe2.md.

Results by model

Model Mean score Completed Input Output Cache read Cache write Tokens processed† Wall-clock Run cost Cost basis*
claude-opus-4-8 79.12 5/5 29,721 743,666 147,727,073 1,463,549 149,964,009 178.1m $135.69 metered (Bedrock)
claude-sonnet-5 76.96 5/5 27,739 1,062,850 366,969,078 2,191,148 370,250,815 276.7m $181.30 metered (Bedrock)
claude-opus-5 76.00 5/5 47,618 653,865 82,266,236 1,978,386 84,946,105 241.1m $180.73 metered (Bedrock)
kimi-k2.7-code 73.69 4/5 8,896,632 122,386 0 0 9,019,018 51.2m $3.28 hardware-derived (p5en.48xlarge)
glm-5.2 72.75 5/5 11,352,314 205,272 0 0 11,557,586 73.2m $5.61 hardware-derived (p5en.48xlarge)
deepseek-v3.2 52.20 5/5 40,665,827 250,576 0 0 40,916,403 80.3m $10.53 hardware-derived (p5en.48xlarge)
minimax-m2.5 51.35 5/5 6,194,113 61,230 0 0 6,255,343 10.1m $0.47 hardware-derived (p5en.48xlarge)
qwen3.6-35b 50.32 5/5 23,331,803 209,391 0 0 23,541,194 88.4m $0.80 hardware-derived (p5en.48xlarge)
nemotron-ultra-550b 50.20 4/5 32,598,732 204,053 0 0 32,802,785 70.2m $6.28 hardware-derived (p5en.48xlarge)
gemma-4-31b 48.40 5/5 24,682,418 160,182 0 0 24,842,600 213.0m $4.88 hardware-derived (p5en.48xlarge)
claude-haiku-4-5 45.64 5/5 293 137,405 19,785,473 580,026 20,503,197 21.6m $6.15 metered (Bedrock)
qwen3-coder-480b 44.95 4/5 66,047,338 186,498 0 0 66,233,836 68.2m $15.63 hardware-derived (p5en.48xlarge)
devstral-2-123b 43.12 5/5 27,990,077 128,406 0 0 28,118,483 50.6m $3.81 hardware-derived (p5en.48xlarge)
qwen3-coder-30b 30.20 4/5 50,532,008 193,911 0 0 50,725,919 84.2m $3.63 hardware-derived (p5en.48xlarge)
qwen3-coder-next -- (0 scored) 0/1 0 0 0 0 0 3.1m -- hardware-derived

* Cost basis differs by row and the dollars are NOT directly comparable. hardware-derived (throughput) (self-hosted vLLM): a rented GPU has no per-token bill, so cost is the model's blended cost-per-token -- measured by the p5en.48xlarge throughput sweep at peak concurrency -- times the tokens this run processed. EVERY self-hosted row is priced on that one sweep, including models served on a smaller g6e.12xlarge box, so the fleet shares a single basis and the dollars compare with each other. A row is therefore the cost of that model's work on p5en, not a quote for the box it happened to run on. This prices the real work done, unlike a wall-clock estimate that would also charge idle agent-thinking time. metered (Bedrock): a hosted API's real per-token bill, summed over the run. It is a metered invoice, not a hardware estimate, and (unlike the self-hosted rows) it benefits from Bedrock prompt caching. See cost-per-task-methodology.md.

Tokens processed counts input + output + cache-read + cache-write -- all tokens the model actually processed, not just fresh input+output. On the Bedrock path a task often reports only ~2 fresh input tokens with the rest served from prompt cache, so counting input+output alone would understate the real work ~100x. (Self-hosted rows report their cache reuse via server-side Prometheus counters, folded in here where present.)

A task scoring 0 (missing/empty artifacts) is a model failure, excluded from the mean but counted in Completed. A model with 0 scored tasks did not complete any task under this harness.

Charts

Cost vs. quality (Pareto frontier)

Cost vs quality, Claude Code harness

Quality by dimension (radar)

Quality radar, Claude Code harness

Cost vs. accuracy (bubble area = tokens)

x = cost per task, y = mean score, bubble area = total tokens processed, color = hosting basis (metered Bedrock vs hardware-derived self-hosted -- NOT directly comparable as raw dollars; see the cost note above).

Cost vs accuracy, Claude Code harness