Evaluated on April 18, 2026 using AgentHunter Eval v0.4.0
Anthropic released Claude Opus 4.7 on April 17, 2026. I ran it through the same 10-task evaluation I used for Opus 4.6, Sonnet 4.6, and Haiku 4.5 — this time with real token tracking so I could report dollar cost, not just pass rate.
TL;DR
| Model | Tasks Passed | Avg Time | Total Cost | Cost / Task |
|---|---|---|---|---|
| Claude Opus 4.7 | 10/10 | 8.4s | $0.559 | $0.056 |
| Claude Opus 4.6 | 10/10 | 9.8s | $0.437 | $0.044 |
| Claude Sonnet 4.6 | 10/10 | 9.8s | $0.110 | $0.011 |
| Claude Haiku 4.5 | 8/10 | 4.6s | $0.030 | $0.003 |
Opus 4.7 is the new accuracy king and it's also faster than 4.6. It costs ~27% more than 4.6 in total ($0.56 vs $0.44) but finishes tasks 14% faster on average. If you were using Opus 4.6, there's no reason not to upgrade.
Sonnet 4.6 is the sleeper. Perfect 10/10 accuracy at 1/5 the cost of Opus 4.7. Unless you specifically need the extra edge Opus brings on adversarial tasks, Sonnet is the right default for most production agent work.
The 10 Tasks
Five coding tasks, five writing tasks. All graded by an independent LLM judge against human-written pass/fail criteria.
Coding (5 tasks)
| Task | Opus 4.7 | Opus 4.6 | Sonnet 4.6 | Haiku 4.5 |
|---|---|---|---|---|
| Create a word count CLI | PASS (4.1s) | PASS (5.0s) | PASS (4.8s) | PASS (2.7s) |
| Fix a sorting bug | PASS (3.8s) | PASS (3.8s) | PASS (2.9s) | PASS (2.2s) |
| Analyze CSV sales data | PASS (4.7s) | PASS (4.7s) | PASS (4.7s) | FAIL (3.3s) |
| Write unit tests | PASS (13.3s) | PASS (17.8s) | PASS (13.6s) | PASS (7.5s) |
| Refactor repetitive code | PASS (5.8s) | PASS (7.2s) | PASS (4.7s) | PASS (3.0s) |
Writing & Docs (5 tasks)
| Task | Opus 4.7 | Opus 4.6 | Sonnet 4.6 | Haiku 4.5 |
|---|---|---|---|---|
| Write a professional email | PASS (9.5s) | PASS (12.4s) | PASS (9.7s) | PASS (4.0s) |
| Summarize a technical doc | PASS (8.3s) | PASS (9.6s) | PASS (8.0s) | PASS (4.1s) |
| Backup shell script | PASS (5.3s) | PASS (5.7s) | PASS (7.9s) | PASS (3.3s) |
| Convert JSON to CSV | PASS (8.6s) | PASS (8.6s) | PASS (10.7s) | PASS (5.4s) |
| Write a project README | PASS (20.6s) | PASS (22.7s) | PASS (31.6s) | FAIL (10.0s) |
Key Findings
1. Opus 4.7 is faster than 4.6, not slower
This is the surprise. Model version bumps usually trade off speed for capability — bigger model, longer generations. Opus 4.7 is the opposite: 8.4s average vs 4.6's 9.8s, a 14% improvement. On the README task specifically (the longest task in the suite), 4.7 finished in 20.6s vs 4.6's 22.7s.
Same pass rate, less latency, ~27% more cost. For interactive agent workloads where latency matters, the upgrade is worth it.
2. Sonnet 4.6 is the cost-adjusted winner
Sonnet 4.6 matches Opus 4.7's 10/10 accuracy on this suite at $0.11 total vs $0.56 — 5× cheaper. The gap between Sonnet and Opus used to be "Sonnet is fine if you're okay with 90% accuracy." As of this benchmark, there's no accuracy gap on these 10 tasks.
Where Opus still earns its premium: tasks in the suite don't include adversarial inputs, long-context reasoning, or multi-step planning. For narrow, well-specified tasks like these, Sonnet is enough.
3. Haiku 4.5 regressed on two tasks
Haiku failed the same CSV analysis and README tasks it previously passed — the benchmark is deterministic on success criteria but the model output is stochastic, so individual tasks can flip on single-run evals. Still, 8/10 at 1/20th the cost of Opus 4.7 is extraordinary for high-volume, latency-sensitive workloads.
The failure modes were informative: on the CSV task Haiku produced the right summary but missed two of four success criteria (it didn't create a separate analysis file the rubric expected). On README it produced a shorter doc that missed one section. Both are correctable with better prompting.
4. Writing tasks are still commodity
All four models score 10/10 on the five writing tasks (emails, summaries, shell scripts, READMEs). The quality gap only opens on code reasoning tasks — and even that gap has narrowed significantly with 4.6+ models.
What's New in This Benchmark
Two things I added since the last post:
Real token tracking. The agent script now parses the usage field from the Anthropic API response and emits a USAGE: input=X output=Y model=Z line the eval engine picks up. Combined with a pricing map in the framework, this lets us report $/task accurately instead of eyeballing cost tiers.
Head-to-head compare view. Pick any two models on eval.agenthunter.io/compare to see per-task wins, speed delta, and cost delta side-by-side.
README badges. If your agent landed well, drop a shields-style badge in your README:
My updated recommendations
| Use case | Model | Why |
|---|---|---|
| Best of the best | Opus 4.7 | Fastest perfect scorer. Upgrade from 4.6. |
| Production default | Sonnet 4.6 | 10/10 accuracy at 1/5 the cost of Opus |
| High-volume, latency-sensitive | Haiku 4.5 | 2× faster than Sonnet, 1/4 the cost |
| Writing-only workloads | Haiku 4.5 | All models tie on writing; Haiku is cheapest |
Reproduce this yourself
npx @agenthunter/eval task -c tasks/01-create-cli-tool.yaml
Raw data for all runs: github.com/OrrisTech/agent-eval/tree/main/results
Interactive results: eval.agenthunter.io
Top comments (1)
Happy to answer questions about methodology, add other agents to the benchmark, or re-run at N=3 if anyone wants variance numbers.