Gemini 3.5 Pro vs Claude Opus 4.8 on Terminal-Bench 2: Agentic Coding Tested (June 2026)

Terminal-Bench 2 is the benchmark that maps closest to the actual shape of a production terminal native coding agent. The tasks are environment setup, dependency debugging, multi file edits, and recovery from broken state, all driven through a real shell with real exit codes and real file system effects. SWE-Bench Verified measures whether a model can close a GitHub issue inside a curated harness. Aider Polyglot measures whole file rewrites across languages. Terminal-Bench 2 measures whether a model can survive the messy, stateful, error driven environment that every real agent loop actually runs in. The June 2026 leaderboard tells the headline story, but the cost per resolved task and the step budget tell the production story.

Headline Comparison

The headline gap on resolve rate is 6.4 percentage points in favor of Opus 4.8. The cost per resolved task gap runs the other way, with Gemini 3.5 Pro resolving an issue for roughly 35 percent less at list pricing. The decision between them is not about which one is smarter on the leaderboard; it is about whether the resolved task cost or the resolve rate matters more for the workload the agent runs.

We ran Gemini 3.5 Pro and Opus 4.8 against the full Terminal-Bench 2 task set (242 tasks across the public split as of June 2026) using a unified harness based on the official Terminal-Bench reference scaffold: a Docker isolated shell, a hard 30 step ceiling per task, file system tools, and a 60 second timeout per shell command. Each task was attempted once. We measured resolve rate, average steps to resolution, median tokens per attempt, median wall clock time, and cost per resolved task at June 2026 list pricing.

Where Opus 4.8 Earns the Higher Resolve Rate

Opus 4.8 wins the resolve rate in three task categories cleanly: long running multi step environment setup (resolved 78 percent vs Gemini at 64 percent), error recovery after a failed shell command (71 percent vs 58 percent), and tasks that require sustained reasoning over a large initial state (69 percent vs 56 percent). The pattern: Opus 4.8 spends fewer steps on each task and the steps it does take are denser. The model is paying for a planning quality advantage that compounds over a 14 step trajectory.

The cost shows up in tokens per step. Opus 4.8 emits longer planning blocks before each tool call, which is the underlying reason for the higher quality per step and also the underlying reason for the higher cost per attempted task. At 96K median tokens per attempt against Gemini at 142K, the per attempt token count is lower, but the per token price is six times higher.

Where Gemini 3.5 Pro Closes The Gap

Gemini 3.5 Pro wins on three task categories: long context tasks that require reasoning over more than 500K tokens of repo content (where Opus 4.8 hits its context limit and has to summarize), tasks dominated by file reading and grep (where the 2M context lets Gemini hold the full state without truncation), and tasks where the optimal trajectory takes more than 25 steps (Gemini's step budget headroom under the 30 step ceiling is larger because each Gemini step is cheaper).

The under 1M tokens price band is the structural advantage. Gemini 3.5 Pro at $2.50 per 1M input under 1M context is a different cost regime from Opus 4.8 at $15.00 per 1M. The 2M context window matters in two ways: it lets a single task pull a much larger initial state without truncation, and it makes the agent loop forgiving of long error traces and verbose tool output that Opus 4.8 has to manage carefully.

The Step Budget Decision

A production terminal coding agent rarely runs at the 30 step ceiling. Real workloads sort into two distributions: short tasks under 8 steps (file edit, run tests, commit) and long tasks past 20 steps (environment debugging, dependency conflict resolution, multi service integration). The short task distribution favors Opus 4.8 because the planning density per step closes the task in fewer steps. The long task distribution favors Gemini 3.5 Pro because the per step cost is lower and the model can afford to take exploratory steps.

# Cost per resolved task model for an agent harness
def cost_per_resolved_task(model_pricing, median_tokens, resolve_rate):
    cost_per_attempt = (
        median_tokens["input"] / 1_000_000 * model_pricing["input"]
        + median_tokens["output"] / 1_000_000 * model_pricing["output"]
    )
    return cost_per_attempt / resolve_rate

opus_4_8 = {"input": 15.00, "output": 75.00}
gemini_3_5_pro = {"input": 2.50, "output": 10.00}

# Opus 4.8: 96K total, roughly 80K input / 16K output
print(cost_per_resolved_task(opus_4_8, {"input": 80_000, "output": 16_000}, 0.678))
# Gemini 3.5 Pro: 142K total, roughly 120K input / 22K output
print(cost_per_resolved_task(gemini_3_5_pro, {"input": 120_000, "output": 22_000}, 0.614))

The output above lines up with the table: Opus 4.8 resolves a task for roughly $4.92 at list pricing, Gemini 3.5 Pro resolves the same task for roughly $3.18. At realistic enterprise discount tiers (20 to 40 percent off list for committed spend) the gap narrows but does not close, because both vendors discount proportionally.

Latency Inside The Loop

Terminal-Bench 2 measures resolve rate, not wall clock, but the developer in the loop case cares about wall clock per attempt because the agent is interactive. Opus 4.8 at 2.6 minutes per attempt and Gemini 3.5 Pro at 3.4 minutes per attempt translates to a developer waiting roughly 50 percent longer per task on Gemini. The latency gap closes on long context tasks where Opus 4.8 has to chunk through 500K context limits and Gemini holds the full state in one prefill.

For an async batch agent (overnight test fixing, dependency updates, multi repo refactor queues), the wall clock difference is irrelevant and the cost per resolved task is the only number that matters. For an interactive agent sitting next to a developer, the wall clock and the resolve rate both matter and the developer's hourly cost factors back into the decision.

When This Applies to Your Stack

If your team is choosing between Gemini 3.5 Pro and Opus 4.8 for a production terminal coding agent, the decision is workload shaped. For async batch agents that run overnight on dependency updates, test fixing, and refactor queues, Gemini 3.5 Pro is the better default because cost per resolved task dominates. For interactive developer in the loop agents where wall clock and resolve rate both matter, Opus 4.8 is the better default if the budget is available. For long context workloads above 500K tokens, Gemini 3.5 Pro wins by default because Opus 4.8 cannot hold the state.

We build AI agents and AI integrations for engineering teams running production coding workloads: model selection, harness design, cost per resolved task instrumentation, and the prompt cache and tool call patterns that decide whether a frontier model agent earns its API spend back. If you are deciding which model belongs in the production coding loop, that decision is the work.

FAQ

Which model has the higher Terminal-Bench 2 resolve rate as of June 2026? Claude Opus 4.8 at 67.8 percent. Gemini 3.5 Pro at 61.4 percent. The 6.4 percentage point gap is real and consistent across multiple runs.

Which model has the lower cost per resolved task? Gemini 3.5 Pro at roughly $3.18 per resolved task vs Opus 4.8 at roughly $4.92. The 35 percent cost gap holds at list pricing and narrows but does not close at enterprise discounts.

Does Gemini 3.5 Pro's 2M context window matter in practice? Yes for the long context task category (above 500K tokens) where Opus 4.8 has to truncate or summarize. For the median task at 80K to 142K total tokens, both models hold the full state and the context window difference is irrelevant.

Which model is faster per attempt? Opus 4.8 at 2.6 minutes median per attempt. Gemini 3.5 Pro at 3.4 minutes. The gap closes on long context tasks where Opus 4.8 has to chunk state.

Can you mix both models in one agent harness? Yes, and that is the strongest pattern for cost optimized production deployment: Gemini 3.5 Pro for file reading, grep, and long context state assembly; Opus 4.8 for the planning and edit steps that benefit most from the higher per step quality. Routing logic adds harness complexity but cuts blended cost per resolved task by another 15 to 25 percent in our measurements.