What Is MBPP Coding Benchmark?

What Is MBPP Coding Benchmark?

MBPP, short for Mostly Basic Python Problems, is an LLM evaluation benchmark for measuring whether a model can produce short Python programs that pass unit tests. It shows up in an eval pipeline before production coding agents, where teams compare models with pass@1, pass@k, compile-error rate, and hidden-test pass rate. In FutureAGI, MBPP is best treated as a benchmark dataset paired with regression evals, traceAI-instrumented agent runs, and task-specific code-execution metrics.

Why MBPP matters in production LLM and agent systems

MBPP matters because coding models often fail at the boundary between plausible code and executable code. A model can explain a loop, write a readable helper, and still miss an edge case, return the wrong type, or generate code that never compiles. If a team ignores MBPP-style checks, coding assistants ship with silent correctness gaps: happy-path demos pass, but generated patches fail hidden tests, CI, or customer-specific inputs.

Developers feel that as review debt. They stop trusting completions and spend time writing the tests the model should have satisfied. SREs feel it when a coding agent keeps retrying a broken script, burns tool budget, or opens a pull request that fails every pipeline job. Product teams see lower adoption because users learn that “looks right” is not the same as “runs.”

In 2026 multi-step coding agents, the signal is more useful when paired with trace data. The base model may pass simple MBPP tasks, while the agent fails after choosing the wrong file, skipping tests, or using a tool output stale by one step. Watch for compile-error rate, first-attempt test pass rate, repair attempts per task, tool-call failure rate, and eval-fail-rate-by-cohort after a prompt, model, or router change. Compared with HumanEval, MBPP uses more natural-language problem statements and a broader set of beginner-level Python tasks; compared with SWE-bench, it is still a small-function benchmark, not full-repository repair.

How FutureAGI handles MBPP

MBPP has no dedicated FutureAGI surface in the inventory, so the clean implementation is a benchmark workflow rather than a named evaluator. FutureAGI’s approach is to keep MBPP execution-grounded: load each task into fi.datasets.Dataset, store the model completion as the response, run the unit-test harness in a sandbox, and write the pass/fail result back as an evaluation row through Dataset.add_evaluation.

A practical example: a team evaluating a coding agent built on OpenAI Agents instruments live runs with the openai-agents traceAI integration. Nightly, it runs MBPP against the candidate model and stores pass@1, compile-error rate, timeout rate, and failing test identifiers beside the model name, prompt version, and sampling settings. In the same dashboard, production traces show llm.token_count.prompt, tool-call status, and the number of repair attempts before a task passes CI.

The nearest FutureAGI evaluator classes are ContainsCode and GroundTruthMatch. Use ContainsCode to catch empty or explanation-only completions, and GroundTruthMatch for deterministic expected-output checks around harness metadata. Do not treat either as a substitute for executing MBPP unit tests. If MBPP pass@1 drops after a router change in Agent Command Center, roll back the routing policy: cost-optimized, pin a model fallback, or block the release with a regression eval.

How to measure or detect MBPP

Measure MBPP with sandboxed execution. The core question is whether generated code passes tests, not whether it reads well.

• pass@1: fraction of tasks solved by the first generated completion; the most user-visible release-gate number.
• pass@k: probability that at least one of k sampled completions passes; useful for search-based coding systems.
• compile-error and timeout rate: generations that fail before assertions run; often reveal prompt, dependency, or sandbox regressions.
• eval-fail-rate-by-cohort: FutureAGI dashboard signal grouped by model, prompt version, task category, or agent route.
• ContainsCode and GroundTruthMatch: adjacent FutureAGI evaluator classes for code-presence sanity checks and deterministic expected-output checks around the harness.

from fi.evals import GroundTruthMatch

metric = GroundTruthMatch()
result = metric.evaluate(
    response="return len([x for x in nums if x % 2 == 0])",
    expected_response="return len([x for x in nums if x % 2 == 0])",
)
print(result.score)

This snippet does not replace MBPP. It shows how to attach a deterministic evaluator near the benchmark harness; MBPP still requires executing candidate code in a sandbox.

Common mistakes

Most MBPP errors come from treating it as a leaderboard shortcut instead of an executed regression suite. The recurring mistakes are concrete:

• Calling MBPP production readiness. It covers short Python tasks, not repository navigation, dependency APIs, code review, CI repair, or safe tool use.
• Reporting pass@k without pass@1. pass@k can hide weak first-attempt quality when the product cannot sample and rank many completions.
• Letting unit tests leak into prompts. Any visible hidden test turns MBPP from generation into memorization and inflates the pass rate.
• Treating syntax success as correctness. Code that compiles but fails edge-case tests is still a failed benchmark item.
• Changing decoding settings mid-comparison. Temperature, sample count, timeout, and sandbox policy must stay fixed across model runs.