LiveBench Agentic Coding: Terra Leads at 68.0%, Fable 5 Posts the Worst Score in the Frontier Tier at 46.9%
When GPT-5.6 launched, the narrative was simple: Sol leads on intelligence, Luna leads on speed and cost, and Terra occupies an awkward middle that neither wins on price nor on ceiling. The July 2026 LiveBench data breaks that narrative on the metric that matters most for production agent deployments: agentic coding specifically.
The Agentic Coding Numbers
| Model | Agentic Coding | Cost/Task |
|---|---|---|
| GPT-5.6 Terra Max Effort | 68.0% | $0.497 |
| GPT-5.6 Sol Max Effort | 65.6% | $0.589 |
| Claude Opus 5 xHigh | 61.3% | $0.487 |
| GPT-5.5 xHigh | 52.1% | $0.530 |
| Claude Fable 5 Max Effort | 46.9% | $1.573 |
Terra leads every frontier model on agentic coding by 2.4 points over Sol, 6.7 points over Opus 5, and 21.1 points over Fable 5. It does this at $0.497 per successful task — cheaper than Sol ($0.589) and just fractionally above Opus 5 ($0.487).
Fable 5 is the outlier. At 46.9% agentic coding and $1.573 per task, it costs 3.2x Terra’s price per task to score 31% lower on the benchmark that agent builders actually care about. This confirms the finding covered earlier — Fable 5 Max Effort posts agentic coding’s weakest score in the frontier tier — but the comparison against Terra makes the gap harder to ignore.
What LiveBench Measures
LiveBench agentic coding is a real-world coding agent test: the model must understand a codebase, write or modify code that satisfies requirements, and pass test suites. It is closer to production SWE-bench than to simple code completion, and the gap between overall intelligence and agentic coding performance is real and significant.
Terra’s overall LiveBench score is 79.8, below Fable 5’s 80.8 and Sol’s 82.4. On general intelligence tasks — reasoning, math, language — those two points matter. On agentic coding tasks specifically, Terra’s architecture advantage converts to the top score.
Why Terra Leads Here
The most plausible explanation is training distribution. Terra appears to have been optimized more heavily for instruction-following under code constraints than for the broad reasoning ceiling that Sol targets. SWE-bench Pro shows Terra at 68.0% agentic coding versus Sol’s 65.6% — a consistent gap across evaluation methods.
The implication for teams building coding agents: the model that wins on intelligence benchmarks is not necessarily the model that maximizes agentic coding task success rate. Terra at $0.497/task beats Sol at $0.589/task on both dimensions for this specific workload.
Cost Structure at Scale
The cost-per-task numbers compress the analysis into a single decision signal. If your workload is agentic coding:
- Terra: best score, second-cheapest cost per task in the frontier tier
- Opus 5: near-matching cost, 6.7 points lower
- Sol: 18% more expensive per task, 2.4 points lower
- Fable 5: 3.2x more expensive per task, 31% lower
The August routing question for teams running high-volume coding agents is whether Terra’s 2.4-point agentic coding lead over Sol justifies routing decisions, or whether the difference is within noise on their specific task distribution. What is not a close call is Fable 5 at $1.573/task for the lowest agentic coding score in the top tier.