{
 "body": "\nPart of the [MultiPL-E](multipl_e.md) family.\n\n## What it measures\n\nGo is a compiled, statically typed, garbage-collected language designed at Google for simple concurrency and fast builds. MultiPL-E translates the HumanEval and MBPP prompts into Go by rewriting\nthe function signature, docstring and tests with Go's own syntax and type system, then asks the\nmodel to complete the function body. The nuprl/MultiPL-E dataset card lists 154 HumanEval-derived items\nand 374 MBPP-derived items for Go, both a little short of the original Python pools because a\nhandful of problems could not be ported faithfully. Completions run against a real Go toolchain\ninside a container and are checked against the translated tests; there is no partial credit for a\ncompletion that fails to compile or fails any test.\n\n## Reading the numbers\n\nA high pass@1 here shows a model can produce working Go for short, self-contained problems whose\nlogic it likely already knows from Python; it does not test Go-specific idiom, library or ecosystem\nknowledge beyond what one function needs. Compare this score against `multipl_e_python` and other\nMultiPL-E language pages for the same model: a large gap usually reflects less Go in the model's\ntraining data rather than a difference in reasoning ability. See the [MultiPL-E](multipl_e.md) family\npage for dataset licence, contamination and harness details shared by every language in the family.\n",
 "build": {
  "built_at": "2026-09-09T16:56:50+00:00",
  "commit": "0a599558854c0e238c03a0f0d725239cb28f9d11",
  "eligibility_as_of": "2026-09-09"
 },
 "disposition": {
  "canonical_id": "multipl_e_go",
  "reasons": [],
  "status": "unassessed",
  "verified_results": []
 },
 "models_covered": [
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Claude Opus 4",
   "model_id": "anthropic/claude-opus-4-20250514",
   "provider": "anthropic",
   "provider_display": "Anthropic",
   "score": 85.5,
   "source": "lmarena.ai, provider-reports, multimodal-evals, safety-evals, preference-evals, domain-evals"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Claude Opus 4.6",
   "model_id": "anthropic/claude-opus-4-6",
   "provider": "anthropic",
   "provider_display": "Anthropic",
   "score": 85.5,
   "source": "lmarena.ai, provider-reports, multimodal-evals, safety-evals, preference-evals, domain-evals, anthropic-system-card-mythos"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Claude Sonnet 4",
   "model_id": "anthropic/claude-sonnet-4-20250514",
   "provider": "anthropic",
   "provider_display": "Anthropic",
   "score": 82.8,
   "source": "lmarena.ai, provider-reports, multimodal-evals, safety-evals, preference-evals"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Claude Sonnet 4.5",
   "model_id": "anthropic/claude-sonnet-4-5-20250929",
   "provider": "anthropic",
   "provider_display": "Anthropic",
   "score": 82.8,
   "source": "lmarena.ai, provider-reports, multimodal-evals, safety-evals, preference-evals"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Claude Sonnet 4.5 (latest)",
   "model_id": "anthropic/claude-sonnet-4-5",
   "provider": "anthropic",
   "provider_display": "Anthropic",
   "score": 82.8,
   "source": "lmarena.ai, provider-reports, multimodal-evals, safety-evals, preference-evals"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "GPT-4.1",
   "model_id": "openai/gpt-4-1",
   "provider": "openai",
   "provider_display": "OpenAI",
   "score": 82.1,
   "source": "lmarena.ai, provider-reports, multimodal-evals, safety-evals, domain-evals preference-evals, llm-stats, intlpull"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Gemini 2.5 Pro",
   "model_id": "google/gemini-2-5-pro",
   "provider": "google",
   "provider_display": "Google DeepMind",
   "score": 81.2,
   "source": "lmarena.ai, provider-reports, multimodal-evals, safety-evals, preference-evals, domain-evals, llm-stats, intlpull"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "DeepSeek R1",
   "model_id": "deepseek/deepseek-r1",
   "provider": "deepseek",
   "provider_display": "DeepSeek",
   "score": 80.5,
   "source": "lmarena.ai, provider-reports, preference-evals, open-llm-leaderboard-v2, domain-evals"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "DeepSeek R1 0528",
   "model_id": "deepseek/deepseek-r1-0528",
   "provider": "deepseek",
   "provider_display": "DeepSeek",
   "score": 80.5,
   "source": "lmarena.ai, provider-reports, preference-evals, open-llm-leaderboard-v2, domain-evals"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "DeepSeek R1 0528 NVFP4 v2",
   "model_id": "nvidia/deepseek-r1-0528-nvfp4-v2",
   "provider": "nvidia",
   "provider_display": "NVIDIA",
   "score": 80.5,
   "source": "lmarena.ai, provider-reports, preference-evals, open-llm-leaderboard-v2"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "DeepSeek Reasoner",
   "model_id": "deepseek/deepseek-reasoner",
   "provider": "deepseek",
   "provider_display": "DeepSeek",
   "score": 80.5,
   "source": "lmarena.ai, provider-reports, preference-evals, open-llm-leaderboard-v2, llm-stats"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "GPT-4o",
   "model_id": "openai/gpt-4o",
   "provider": "openai",
   "provider_display": "OpenAI",
   "score": 79.1,
   "source": "lmarena.ai, provider-reports, multimodal-evals, safety-evals, preference-evals, domain-evals, llm-stats, intlpull"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "GPT-4o (2024-05-13)",
   "model_id": "openai/gpt-4o-2024-05-13",
   "provider": "openai",
   "provider_display": "OpenAI",
   "score": 79.1,
   "source": "lmarena.ai, provider-reports, multimodal-evals, safety-evals, preference-evals, domain-evals, llm-stats, intlpull"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "GPT-4o (2024-08-06)",
   "model_id": "openai/gpt-4o-2024-08-06",
   "provider": "openai",
   "provider_display": "OpenAI",
   "score": 79.1,
   "source": "lmarena.ai, provider-reports, multimodal-evals, safety-evals, preference-evals, domain-evals, llm-stats, intlpull"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "GPT-4o (2024-11-20)",
   "model_id": "openai/gpt-4o-2024-11-20",
   "provider": "openai",
   "provider_display": "OpenAI",
   "score": 79.1,
   "source": "lmarena.ai, provider-reports, multimodal-evals, safety-evals, preference-evals, domain-evals, llm-stats, intlpull"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "GPT-4o mini",
   "model_id": "openai/gpt-4o-mini",
   "provider": "openai",
   "provider_display": "OpenAI",
   "score": 79.1,
   "source": "lmarena.ai, provider-reports, multimodal-evals, safety-evals, preference-evals, domain-evals, llm-stats, intlpull"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Qwen2.5 Coder 32B Instruct",
   "model_id": "qwen/qwen2-5-coder-32b-instruct",
   "provider": "qwen",
   "provider_display": "Alibaba / Qwen Team",
   "score": 76.5,
   "source": "provider-reports, open-llm-leaderboard-v2"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Qwen2.5 Coder 32B Instruct AWQ",
   "model_id": "qwen/qwen2-5-coder-32b-instruct-awq",
   "provider": "qwen",
   "provider_display": "Alibaba / Qwen Team",
   "score": 76.5,
   "source": "provider-reports, open-llm-leaderboard-v2"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Gemma 4 31B",
   "model_id": "google/gemma-4-31b",
   "provider": "google",
   "provider_display": "Google DeepMind",
   "score": 73.2,
   "source": "lmarena.ai, provider-reports, multimodal-evals, open-llm-leaderboard-v2"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "gemma 4 31B it",
   "model_id": "google/gemma-4-31b-it",
   "provider": "google",
   "provider_display": "Google DeepMind",
   "score": 73.2,
   "source": "lmarena.ai, provider-reports, multimodal-evals, open-llm-leaderboard-v2"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "gemma 4 31B it GGUF",
   "model_id": "unsloth/gemma-4-31b-it-gguf",
   "provider": "unsloth",
   "provider_display": "Unsloth",
   "score": 73.2,
   "source": "lmarena.ai, provider-reports, multimodal-evals, open-llm-leaderboard-v2"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Gemma 4 31B IT NVFP4",
   "model_id": "nvidia/gemma-4-31b-it-nvfp4",
   "provider": "nvidia",
   "provider_display": "NVIDIA",
   "score": 73.2,
   "source": "lmarena.ai, provider-reports, multimodal-evals, open-llm-leaderboard-v2"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Codestral (latest)",
   "model_id": "mistral/codestral-latest",
   "provider": "mistral",
   "provider_display": "Mistral AI",
   "score": 72.5,
   "source": "lmarena.ai, provider-reports"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Mistral Large (latest)",
   "model_id": "mistral/mistral-large-latest",
   "provider": "mistral",
   "provider_display": "Mistral AI",
   "score": 72.1,
   "source": "lmarena.ai, provider-reports, multimodal-evals, safety-evals, preference-evals, open-llm-leaderboard-v2, llm-stats"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Mistral Large 2.1",
   "model_id": "mistral/mistral-large-2411",
   "provider": "mistral",
   "provider_display": "Mistral AI",
   "score": 72.1,
   "source": "lmarena.ai, provider-reports, safety-evals, preference-evals,, domain-evals open-llm-leaderboard-v2"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Mistral Large 3",
   "model_id": "mistral/mistral-large-2512",
   "provider": "mistral",
   "provider_display": "Mistral AI",
   "score": 72.1,
   "source": "lmarena.ai, provider-reports, multimodal-evals, safety-evals, preference-evals, open-llm-leaderboard-v2"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Gemma 4 26B",
   "model_id": "google/gemma-4-26b",
   "provider": "google",
   "provider_display": "Google DeepMind",
   "score": 71.5,
   "source": "lmarena.ai, provider-reports, open-llm-leaderboard-v2"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Qwen2.5 Coder 14B Instruct",
   "model_id": "qwen/qwen2-5-coder-14b-instruct",
   "provider": "qwen",
   "provider_display": "Alibaba / Qwen Team",
   "score": 70.1,
   "source": "bigcode-leaderboard, provider-reports, open-llm-leaderboard-v2"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Llama 3.3 70B Instruct NVFP4",
   "model_id": "nvidia/llama-3-3-70b-instruct-nvfp4",
   "provider": "nvidia",
   "provider_display": "NVIDIA",
   "score": 68.5,
   "source": "lmarena.ai, provider-reports, safety-evals, preference-evals, open-llm-leaderboard-v2, llm-stats"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Llama-3.3-70B-Instruct",
   "model_id": "meta/llama-3-3-70b-instruct",
   "provider": "meta",
   "provider_display": "Meta",
   "score": 68.5,
   "source": "lmarena.ai, provider-reports, safety-evals, preference-evals, open-llm-leaderboard-v2, llm-stats"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "phi 4",
   "model_id": "microsoft/phi-4",
   "provider": "microsoft",
   "provider_display": "Microsoft",
   "score": 67.5,
   "source": "lmarena.ai, provider-reports, preference-evals, open-llm-leaderboard-v2,, domain-evals llm-stats"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Phi 4 mini instruct",
   "model_id": "microsoft/phi-4-mini-instruct",
   "provider": "microsoft",
   "provider_display": "Microsoft",
   "score": 67.5,
   "source": "lmarena.ai, provider-reports, preference-evals, llm-stats, open-llm-leaderboard-v2"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Llama 3.1 70B",
   "model_id": "meta/llama-3-1-70b",
   "provider": "meta",
   "provider_display": "Meta",
   "score": 66.8,
   "source": "lmarena.ai, provider-reports, safety-evals, domain-evals, open-llm-leaderboard-v2"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Llama 3.1 70B Instruct",
   "model_id": "meta/llama-3-1-70b-instruct",
   "provider": "meta",
   "provider_display": "Meta",
   "score": 66.8,
   "source": "lmarena.ai, provider-reports, safety-evals, domain-evals, open-llm-leaderboard-v2"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Qwen2.5 Coder 7B Instruct",
   "model_id": "qwen/qwen2-5-coder-7b-instruct",
   "provider": "qwen",
   "provider_display": "Alibaba / Qwen Team",
   "score": 63.8,
   "source": "bigcode-leaderboard, provider-reports, open-llm-leaderboard-v2"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Qwen2.5 Coder 7B Instruct GPTQ Int4",
   "model_id": "qwen/qwen2-5-coder-7b-instruct-gptq-int4",
   "provider": "qwen",
   "provider_display": "Alibaba / Qwen Team",
   "score": 63.8,
   "source": "bigcode-leaderboard, provider-reports, open-llm-leaderboard-v2"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "CodeLlama 34B Instruct hf",
   "model_id": "meta/codellama-34b-instruct-hf",
   "provider": "meta",
   "provider_display": "Meta",
   "score": 55.2,
   "source": "bigcode-leaderboard, provider-reports, open-llm-leaderboard-v1"
  }
 ],
 "page": {
  "aliases": [],
  "category": "coding",
  "contamination": {
   "note": "Inherits the source HumanEval/MBPP contamination risk (public since 2021-2022); see the multipl_e family page for detail.",
   "risk": "high"
  },
  "dataset": {
   "languages": [
    "Go"
   ],
   "license": "MIT",
   "modalities": [
    "code"
   ],
   "public_test_set": true,
   "size": null,
   "size_note": "Reading the nuprl/MultiPL-E dataset card on 2026-09-07: 154 HumanEval-derived items (of the original 164) and 374 MBPP-derived items, both a little short of the full Python originals because a handful of problems could not be ported to Go faithfully.",
   "splits": "test (humaneval-go and mbpp-go configs)",
   "url": "https://huggingface.co/datasets/nuprl/MultiPL-E"
  },
  "freshness": {
   "researched": "2026-09-07",
   "researched_by": "sonnet-5 agent, batch 1, slice C",
   "reviewed": "",
   "reviewed_by": ""
  },
  "harness": {
   "bigbench": "",
   "helm": "",
   "inspect_evals": "",
   "lm_eval": "",
   "opencompass": "",
   "other": "bigcode-evaluation-harness task multiple-go"
  },
  "id": "multipl_e_go",
  "last_updated": "",
  "leaderboard_url": "",
  "lineage": {
   "family": "multipl_e",
   "predecessor": "",
   "successors": [],
   "variants": []
  },
  "measures": "This subset translates the HumanEval and MBPP prompts into Go by rewriting each problem's function signature, docstring and tests with Go syntax and typing, then asks the model to complete the function body in Go. The underlying algorithmic problem is unchanged from the Python original; only the surface language differs.\n",
  "metric": {
   "baseline_note": "No published human baseline.",
   "direction": "higher_is_better",
   "human_baseline": null,
   "max_score": 100,
   "name": "pass@1",
   "random_baseline": 0,
   "unit": "%"
  },
  "name": "MultiPL-E: Go",
  "page_kind": "subset",
  "paper": {
   "arxiv": "2208.08227",
   "title": "MultiPL-E: A Scalable and Extensible Approach to Benchmarking Neural Code Generation",
   "url": "https://arxiv.org/abs/2208.08227",
   "year": 2022
  },
  "publisher": {
   "authors": [
    "Federico Cassano",
    "John Gouwar",
    "Daniel Nguyen",
    "Sydney Nguyen",
    "Luna Phipps-Costin",
    "Donald Pinckney",
    "Ming-Ho Yee",
    "Yangtian Zi",
    "Carolyn Jane Anderson",
    "Molly Q Feldman",
    "Arjun Guha",
    "Michael Greenberg",
    "Abhinav Jangda"
   ],
   "org": "Northeastern University Programming Research Lab (nuprl)",
   "url": "https://github.com/nuprl/MultiPL-E"
  },
  "released": "2022-08",
  "repo_url": "https://github.com/nuprl/MultiPL-E",
  "saturation": {
   "as_of": "",
   "note": "Not established at the per-language level; see the multipl_e family page for the general pattern of high-resource versus low-resource language spread.",
   "status": "unknown",
   "top_score": null
  },
  "sources": [
   {
    "accessed": "2026-09-07",
    "title": "MultiPL-E: A Scalable and Extensible Approach to Benchmarking Neural Code Generation",
    "url": "https://arxiv.org/abs/2208.08227"
   },
   {
    "accessed": "2026-09-07",
    "title": "nuprl/MultiPL-E dataset card",
    "url": "https://huggingface.co/datasets/nuprl/MultiPL-E"
   },
   {
    "accessed": "2026-09-07",
    "title": "nuprl/MultiPL-E repository",
    "url": "https://github.com/nuprl/MultiPL-E"
   }
  ],
  "status": "active",
  "subcategory": "multilingual code generation",
  "summary": "The Go subset of MultiPL-E: HumanEval and MBPP function-completion problems translated into Go and scored with pass@1.",
  "tags": [
   "code-generation",
   "go",
   "pass-at-k",
   "humaneval",
   "mbpp"
  ],
  "task_format": "Function completion in Go: given a translated signature, docstring and (for HumanEval-derived items) doctests, the model generates a function body, which is compiled or interpreted with a real Go toolchain inside a container and checked against translated unit tests.\n"
 }
}