Search interest in opencode benchmarks is spiking, but the benchmark language is noisy. This page is a practical map: what each benchmark family measures, what it misses, and how to avoid false confidence.
Benchmark Landscape Overview
Most benchmark confusion comes from mixing three different categories: benchmark runs done with the OpenCode harness, standardized software engineering benchmarks like SWE-bench, and terminal-native evaluations. They test different skills and use different scaffolding.
| Benchmark | What It Tests | Evidence Quality | How To Use It |
|---|---|---|---|
| OpenCode bench-style runs | OpenCode harness behavior on chosen tasks | Varies by publication quality | Directional signal only unless task list + config are fully disclosed |
| SWE-bench Verified | Issue-level software fixes on curated repos | Medium (known contamination/test issues) | Trend line and rough model ceiling, not final procurement evidence |
| SWE-bench Pro | Harder multi-file, multi-language issue resolution | Higher than Verified for frontier models | Best public benchmark for coding agent comparison in 2026 |
| Terminal-Bench 2.0 | Shell operations, environment management, command recovery | High for terminal-first workflows | Use when evaluating agents for real terminal-heavy work |
OpenCode Bench: Useful, but Usually Non-Standard
When teams search for "OpenCode bench" or "OpenCode benchmarks," they usually want to know: "How well does OpenCode solve real coding tasks?" The challenge is that many claims are harness-specific, not benchmark-standardized.
What to verify before trusting an OpenCode benchmark
Require these five details: exact task list, model version, prompt template, tool permissions, and stopping criteria. Missing any of them makes cross-post comparison unreliable.
Task Set Disclosure
Look for repository names, issue IDs, and pass/fail criteria. A single aggregate score without tasks is weak evidence.
Harness Disclosure
OpenCode mode, tools, max turns, retries, and context strategy should be explicit. Harness choices can move scores by double digits.
Cost and Time
Pass rate alone hides operational reality. Require token spend and time-to-resolution for each task bucket.
| Question | Good Signal | Weak Signal |
|---|---|---|
| Are tasks and repos listed? | Yes, with IDs and reproducible setup | No, only screenshots or aggregate claim |
| Are run constraints disclosed? | Yes, max turns/time/tools explicitly documented | No, opaque run policy |
| Is there a cost breakdown? | Yes, tokens and wall-clock per task | No cost/time details |
| Is there a baseline comparison? | Yes, same tasks against another tool | No baseline |
SWE-bench Context for OpenCode
SWE-bench remains the most cited coding benchmark family, but it requires context. SWE-bench scores can shift materially based on scaffolding and retrieval strategy, not just model quality.
Important caveat on SWE-bench Verified
OpenAI published why they no longer evaluate on SWE-bench Verified due to contamination and test-quality concerns. For 2026 frontier comparisons, favor SWE-bench Pro and reproducible private evals.
| System | SWE-bench Pro | Terminal-Bench 2.0 | Notes |
|---|---|---|---|
| GPT-5.3-Codex (CLI) | 57.0% (agent-system listing) | 77.3% | Strong terminal execution profile |
| Claude Code (Opus 4.5) | 55.4% (agent-system listing) | 65.4% (Opus 4.6) | Higher reasoning depth, lower terminal score |
| Opus 4.6 + WarpGrep | 57.5% (Morph internal) | N/A | Shows scaffolding impact on same base model |
| OpenCode (public standardized entry) | No single canonical public entry | No canonical public entry | Use task-level head-to-head evals |
Published scores are aggregated from benchmark pages and provider/system announcements. Different harnesses are not directly comparable.
Terminal Benchmarks and Why They Matter for OpenCode
OpenCode is primarily a terminal workflow product. If your team uses shell commands for environment setup, debugging, and repo operations, terminal benchmarks are often more predictive than function-level coding tests.
| Question | Why It Matters for OpenCode | Decision Impact |
|---|---|---|
| Can the agent recover from command failures? | Real projects fail fast on env mismatch and bad assumptions | High: affects day-1 trust in terminal agents |
| Can it sequence multi-step shell tasks? | Most production fixes require command chains, not one-shot code output | High: predicts practical throughput |
| Can it minimize wasteful retries? | Retry loops inflate cost and latency | Medium: directly tied to token efficiency |
How to Interpret OpenCode Benchmarks in Practice
Treat benchmark reading as a weighted evidence problem, not a ranking contest. A practical stack for tool selection is:
- Standardized benchmark signal (SWE-bench Pro or similar).
- Terminal workflow signal (Terminal-Bench or internal shell tasks).
- Your own private issue set with production-like constraints.
| If you optimize for... | Primary Benchmark | Secondary Check | Do Not Skip |
|---|---|---|---|
| Issue resolution quality | SWE-bench Pro-style tasks | Private repo eval | Task-level failure analysis |
| Terminal workflow speed | Terminal benchmark tasks | Median time-to-fix on your scripts | Retry-loop and rollback tracking |
| Cost predictability | Token + wall-clock per resolved task | Cross-provider run with same harness | Budget cap + stop criteria |
Run a Practical OpenCode Benchmark in 7 Steps
If published benchmark claims conflict, run your own controlled eval. Keep it small and reproducible.
- Select 20 to 40 historical issues from your own repos.
- Bucket tasks by difficulty and file-count spread.
- Use the same budget caps for each tool run.
- Log pass/fail, wall-clock time, and token cost per issue.
- Track retries, dead-ends, and human intervention events.
- Repeat with two model configurations to test robustness.
- Choose the tool with best resolved-issue-per-dollar, not highest raw score.
FAQ
Are OpenCode benchmarks trustworthy?
They can be trustworthy if the run is fully documented. Without task IDs, harness config, and cost/time logs, treat them as directional content rather than decision-grade evidence.
Why does harness configuration matter so much?
Retrieval quality, tool permissions, retry policy, and context management can change benchmark outcomes substantially even on the same base model. Benchmarks compare systems, not just models.
Should I ignore public benchmark leaderboards?
No. Use them as one input. Then validate with private tasks that match your repo, latency tolerance, and cost constraints.
How should teams track OpenCode benchmark progress over time?
Maintain a fixed internal benchmark suite and rerun monthly with the same methodology. Version your harness config so score movements are attributable and auditable.
Need Better Than Benchmark Percentages?
Benchmarks tell you who passes. They do not guarantee clean merges. Morph Apply takes model output from OpenCode, Codex, or Claude and merges updates into real files with semantic consistency.