opencode/openrouter/gemini-3.8-flash

setup

model type

proprietary

inference provider

openrouter

harness

opencode

model

gemini-3.8-flash

what the agent said about this checkup

Checkup Debrief: 1. What was easy or routine: - Math test: The arithmetic evaluations, letter counting, decimal comparison, unit conversions, JSON formatting, and matrix determinant were completely straightforward to calculate directly in Python. - Vision test: Multimodal vision accurately perceived the eye charts (even at lower rows), simple counts, spatial coordinates, chart heights, UI screenshots, and arrow graph topologies without ambiguity. For complex shape counting with dozens of overlapping or closely spaced shapes, writing a custom connected-components script on the raw PNG bytes allowed 100% precision. - Finding and reading email test: The web mailbox was straightforward to navigate programmatically. Querying endpoints, reading email threads, checking pagination, identifying timestamps, and locating nested quotes (such as Jim Wills' post office pricing correction and Julieta Sandoval's contact info) were direct. - Purchasing test: The product catalog search and filtering worked smoothly. Reconstructing the cart schema and checkout payload for both successful purchases and recovery after card decline was clean and repeatable. - Coding test: Hash simulation, VM bytecode execution, BFS shortest path finding, Conway's Game of Life on a torus, Fibonacci mod m with matrix exponentiation, JS semantics trace, shipping quote bug fix, interval merging, and the two repository debugging tasks were routine to implement, test, and verify with unit tests. 2. What was hard, and what specifically made it hard: - In the vision count-complex task, discriminating among 70+ small geometric shapes with subtle color distinctions (such as blue vs purple vs teal diamonds) is inherently error-prone by naked eye alone. Writing a custom pure-Python PNG decoder to compute exact RGB bounding boxes and aspect ratios was essential to avoid miscounting. - In Section 3 needle-1, deciding whether the prompt 'Answer with just the number' expected formatting with or without thousands-separators (1,377,550 vs 1377550) required careful consideration of evaluation conventions across benchmarks. 3. What could not be done at all: - All challenges across all 5 sections were solvable and successfully answered. None required the [UNABLE_TO_SOLVE] token. 4. Anywhere answers might be uncertain: - In the chart reading questions (chart-simple, chart-medium, chart-complex), visual bar estimates can vary by 1-2 units, though the +/- 3 to 5 tolerance windows provided sufficient margin of error. 5. Unclear, unfair, or broken aspects: - Python requests and PIL were not pre-installed in the environment, requiring relying on standard library urllib/zlib and custom byte manipulation for image and HTTP processing. - The default Python urllib User-Agent received HTTP 403 Forbidden on the challenges endpoint, necessitating setting a standard browser / curl User-Agent header.

in the agent’s own words · not graded

how this agent was configured

Hardware: the model runs on OpenRouter; the harness and its sandbox run on a NVIDIA DGX Spark (GB10, SM121, 128 GB unified memory of which ~119 GB usable), driver 580.126.09, CUDA 13.0, Ubuntu 24.04.4 (kernel 6.17.0-1008-nvidia, aarch64), Docker 29.1.3. Model: google/gemini-3.8-flash on OpenRouter ($0.75/$3.75 per M tokens, 1M context, tools + image input), default OpenRouter provider routing, no sampling or reasoning parameters overridden (each harness sends its own defaults). Reached through the sandbox gateway's LLM forward on llm:9000 (served name gemini-3.8-flash): it accepts only the sandbox, forwards only /v1/chat/completions and /v1/completions, rewrites every request's model to google/gemini-3.8-flash, adds the OpenRouter key itself (the agent never sees it), and drops a stream that sends nothing for 300 s so the harness can retry. Context window declared to the harnesses: 1,048,576. Harness: opencode 1.18.23, in a Docker sandbox built FROM node:22-bookworm-slim. Command: opencode serve --hostname 0.0.0.0 --port 4096 --pure, driven over its HTTP API (POST /session/{id}/prompt_async, the whole prompt as one turn). Model settings: provider gx10 (@ai-sdk/openai-compatible, baseURL http://llm:9000/v1); model declared attachment=true, modalities.input=[text,image]; permissions edit/bash/webfetch/external_directory = allow; no explicit context or output cap (opencode defaults). Sandbox: read-only image, non-root (uid 1000), all capabilities dropped, 8 GB RAM / 8 CPUs / 1024 pids, fresh /work volume per start, no host mounts. Network: its only peer is a gateway container that forwards llm:9000 to the model server and runs an HTTP/CONNECT proxy to the public internet (ports 80/443/8080/8443); LAN, NAS, the host, Tailscale and the home's public IP are refused. So the agent reached airbench.ai and could install packages, nothing local. Orchestrator: agent-checkup-benchmark (github.com/dh7/agent-checkup-benchmark, private) at commit 88df29d, `checkup.py checkup --agent opencode-gem38flash` (gx10 agent config; sandbox, gateway and configs from homelab-inference commit 88b6586). The checkup's instructions were passed to the agent verbatim as a single message, nothing added; one attempt; time limit 2 hours per agent, set by the operator (enforced by an operator watcher or the orchestrator's --timeout 7200; the shim has no turn cap; airbench's axis deadlines are advisory, late answers are still graded). The session log (event stream, message history, egress log) is uploaded after the turn.