Claude Opus 4.8 vs DeepSeek V4 Pro Thinking
Both 30/30 on agentic-core-v1. One costs $7.34. The other costs $0.12.
Head-to-head: agentic-core-v1
Harness: agentic-core-v1 — 10 tasks × 3 runs = 30 total. Binary pass/fail per run. Harness version: openclaw@2026.4.22. Full methodology: what we measure and why.
| Metric | Claude Opus 4.8 | DeepSeek V4 Pro Thinking |
|---|---|---|
| Runs passed / total | 30 / 30 | 30 / 30 |
| Pass rate | 100.0% | 100.0% |
| Campaign cost (30 runs) | $7.34 | $0.12 |
| Cost per run | $0.245 | $0.00400 |
| Cost per passing run | $0.245 | $0.00400 |
| Provider | Anthropic | DeepSeek |
| Campaign date | 2026-06-06 | 2026-06-16 |
Claude Opus 4.8
Only model to achieve 30/30 — no failures on any task in any run
Most expensive per passing run in the dataset at $0.245; task_07 still averaged 36 tool calls
DeepSeek V4 Pro Thinking
30/30 at $0.004/run — 61× cheaper per passing run than Claude Opus 4.8
Thinking token overhead adds latency; reasoning traces are non-zero cost even on simple tasks
Verdict
Claude Opus 4.8 and DeepSeek V4 Pro Thinking match on pass rate (30/30). DeepSeek V4 Pro Thinking costs 61× less per passing run. For cost-sensitive production use, DeepSeek V4 Pro Thinking is the clear choice. Claude Opus 4.8 makes sense where provider ecosystem factors outweigh per-call economics.
About agentic-core-v1
agentic-core-v1 is modelbattles' flagship benchmark harness. Ten tasks drawn from real engineering work: fix a failing test, refactor duplicated code, investigate a production log, add a null guard, trace through a codebase. Each task runs three times per model. A run passes if and only if the checker accepts the output — no partial credit, no manual grading.
The harness is deterministic: same task, same environment, same checker across all models. Scores are comparable. task_09 is the persistent difficulty point — it requires the model to recognise a structurally impossible calculation and refuse to produce a wrong answer instead of looping. Most models fail it at least once.
Read: What we actually measure and why · How to read an agentic-core-v1 score
More comparisons
- Claude Sonnet 4.6 vs Claude Haiku 4.5 — A 1-point score gap and a 16× cost gap — is Sonnet worth it?
- DeepSeek V4 Flash vs Claude Sonnet 4.6 — Same agentic-core-v1 score, 36× cost difference — what does Sonnet actually buy you?
- Mistral Small 4 vs Claude Haiku 4.5 — The two most cost-efficient models in the top tier — one passes more, the other costs less.
- GPT-5.5 Instant vs Claude Sonnet 4.6 — Both mid-tier flagships at 90%+ — OpenAI vs Anthropic on real agentic tasks.
- Devstral 2 123B vs Mistral Small 4 — Mistral's code specialist vs its latest small model — which is better for agentic workloads?