Deterministic Multimodal Benchmarking Reproduces GLM-Image vs GPT-4o, Gemini, Claude, and Qwen2-VL

Reproducing multimodal VLM results is notoriously brittle: small prompt edits, silent model updates, or a different CUDA patch can swing scores by double digits. As GLM-Image enters head-to-head comparisons with GPT-4o, Gemini, Claude, and Qwen2-VL, the only credible claims are those you can rerun—byte for byte—weeks later on fresh hardware. This article shows how to remove hidden confounders with a fully pinned, deterministic protocol that standardizes prompts, decoding, inputs, and infrastructure, then quantifies uncertainty with robust statistics. The goal isn’t just repeatability; it’s fairness across models with different APIs and preprocessing.

We’ll detail the evaluation architecture end to end—version pinning and endpoint locking, hardware and framework determinism, prompt/decoding normalization, input controls for images and short video, strict JSON schema adherence, and statistically sound significance testing. You’ll see comparison tables that capture the control surfaces we fix, plus best practices and executable examples to replicate the setup. By the end, you’ll be able to run apples-to-apples evaluations that withstand scrutiny and reproduce GLM-Image vs leading VLMs on public benchmarks with official scoring.

Architecture/Implementation Details

Version pinning and endpoint locking

• Lock exact model IDs and endpoints before the first run and re-validate before scoring. Capture provider model identifiers (e.g., path or versioned model name) from the official docs for GLM-Image (ZhipuAI Open Platform) and comparators (OpenAI GPT-4o, Anthropic Claude Vision, Google Gemini Vision, Qwen2-VL).
• If a provider uplifts a model or changes a dataset split mid-run, invalidate and rerun the entire comparison. Log the model ID string returned in each response.

Hardware standardization

• Use a reproducible base: Ubuntu LTS container by digest, pinned CUDA/cuDNN, and a single NVIDIA H100 80GB or A100 80GB with MIG disabled; log driver versions, GPU UUID, power limits, and max clocks at start of each job.
• Keep inference topology constant (no tensor parallel changes mid-run). For open/on‑prem models, disable quantization unless it’s part of a specifically labeled experiment.

Framework determinism

• Pin PyTorch and inference stacks (e.g., vLLM, TensorRT-LLM) and set deterministic flags. Fix seeds at Python/Numpy/Torch/CUDA layers; enable cuDNN deterministic paths and control cuBLAS workspace. Acknowledge throughput hits in exchange for repeatability.
• Verify determinism on warmup samples by hashing outputs per item and seed.

Example seed and determinism harness:

# seeds.py
import os, random, numpy as np, torch

SEED = 2026
random.seed(SEED)
os.environ["PYTHONHASHSEED"] = str(SEED)
np.random.seed(SEED)

torch.manual_seed(SEED)
torch.cuda.manual_seed_all(SEED)

# PyTorch reproducibility
import torch.backends.cudnn as cudnn
cudnn.deterministic = True
cudnn.benchmark = False # avoid non-deterministic autotune 

# cuBLAS determinism (set before import in some setups)
os.environ.setdefault("CUBLAS_WORKSPACE_CONFIG", ":16:8") # or ":4096:8" 

Prompting and decoding normalization

• Fix a neutral system prompt across models: “You are a helpful, precise vision-language assistant. Follow instructions exactly. If uncertain, say ‘not sure’. Avoid unsupported claims.”
• Standardize task templates for VQA/GQA, COCO captioning, and RefCOCO grounding so only images and task strings vary.
• Decoding defaults: temperature=0.2, top_p=0.9, top_k off unless required; fixed max tokens and stop sequences per task. For providers with structured modes (e.g., function/JSON), enforce schema and measure invalid-JSON rates.

Input controls for images and short video

• Cap the long side at 2048 px, preserve aspect, and apply consistent letterboxing. Log any provider-imposed lower caps.
• If a model can’t accept 2048 px, use deterministic tiling with fixed overlap and an identical stitching policy across models.
• Multi-image prompts enumerate 1-indexed images in order and bind references in the prompt text to those indices.
• For short video, use the same frame sampling policy for all models that accept multiple images (e.g., K=16 evenly spaced frames) and mark others as “not supported” rather than penalizing them.

Structured outputs and schema adherence

• Where supported, run in strict JSON mode or function/tool-calling; validate with a JSON schema per task and count invalid-JSON occurrences before any retries.
• For models without native JSON mode, enforce a compact schema-oriented prompt suffix and parse with robust, forgiving extractors—but continue tracking invalid-JSON rates to avoid hiding format errors.

Uncertainty quantification and significance testing

• Report 95% CIs using non‑parametric bootstrap over items (percentile or BCa) for continuous metrics (e.g., CIDEr, SPICE, BLEU, ROUGE-L, METEOR via the official COCO Caption toolkit). For binomial outcomes (e.g., accuracy on MMBench/MMMU/VQA/GQA), add Wilson intervals.
• Use multi-seed runs (e.g., 5 seeds) for generative tasks to capture server-side sampling variance and decoding stochasticity (same parameters across models).
• Paired tests: McNemar’s test for accuracy; paired permutation tests for continuous metrics. Correct p-values using Benjamini–Hochberg across multiple datasets/metrics to control false discovery.

Latency and throughput instrumentation

• Record time-to-first-token (TTFT) and time-to-last-token (TLTT) for every request; report p50/p90/p99 per model and task. Sweep concurrency (1, 8, 32) in warmed runs. Log VRAM, host RAM, and GPU power draw for on‑prem; record region, streaming on/off, and batch size for APIs.

API rigor and full-fidelity logs

• Store full request/response bodies and headers, model IDs returned by the endpoint, timestamps, region, and HTTP status. Use idempotency keys where supported to mitigate retries.
• Randomize request order across models to reduce time‑of‑day and burst‑limit artifacts.

Benchmarks and official scoring

• Core perception/reasoning suite: MMBench (broad skills), MM‑Vet (open-ended), MMMU (multi‑discipline reasoning), VQA v2 and GQA (VQA and compositional scene understanding), COCO Captions (CIDEr/SPICE/BLEU/METEOR/ROUGE‑L), and RefCOCO/+/g for grounding (IoU≥0.5 when bounding boxes are supported). Use the official scoring scripts/toolkits to avoid reimplementation drift.
• Cross-check procedural consistency with community harnesses such as VLMEvalKit and LMMS‑Eval, and sanity‑check relative ranks against OpenCompass leaderboards to catch setup regressions.

Comparison Tables

What we fix—and why it matters

Control surface	Fixed setting	Why it matters	Evidence/standard
Model version	Exact model ID/endpoint captured in logs	Prevents silent model drift	Provider docs for GLM-Image, GPT‑4o, Claude, Gemini, Qwen2‑VL
OS/driver/GPU	Ubuntu LTS container digest; CUDA/cuDNN pins; single H100/A100; MIG off	Eliminates performance/accuracy variability from kernels/hardware	NVIDIA/CUDA/Docker docs
Seeds/determinism	Fixed Python/Numpy/Torch/CUDA seeds; cuDNN deterministic; cuBLAS workspace	Makes GPU path repeatable at some perf cost	PyTorch randomness guidance
Prompting	Neutral system prompt; shared task templates	Avoids framing bias and hidden few-shot/context changes	Protocol choice
Decoding	temp=0.2, top_p=0.9, fixed max tokens/stops; top_k off unless required	Normalizes sampling and output length	Protocol choice
Inputs	2048 px cap; consistent letterboxing; deterministic tiling; fixed frame sampling	Equalizes model-visible pixels and temporal coverage	Protocol choice
Structured output	Strict JSON schemas; invalid-JSON rate tracked; native JSON/function modes where available	Penalizes formatting errors fairly; reduces hallucinations	OpenAI JSON/function calling; provider modes
Statistics	Bootstrap 95% CI; Wilson for binomial; McNemar/permutation; BH correction	Distinguishes noise from signal; controls multiple comparisons	SciPy bootstrap; standard paired tests
Telemetry	TTFT/TLTT p50/p90/p99; concurrency sweeps; VRAM/power logs; region capture	Enables reproducible latency/throughput comparisons	Hardware/API logging

Pinning surfaces across model families

Model family	Where to lock version	Notes
GLM‑Image (ZhipuAI)	Model ID in ZhipuAI Open Platform API and response logs	Capture the returned model identifier per call
GPT‑4o (OpenAI)	“model” field for the endpoint; log returned model/version	JSON mode/function calling available for schema enforcement
Claude Vision	Model name in API; log response headers and model info	Vision input documented in Claude vision guide
Gemini Vision	Model string (e.g., gemini-…); log region and model	Vision/multimodal behavior in Gemini docs
Qwen2‑VL	Model checkpoint/tag (for open) or API model name	Open-source configs and limits on GitHub

Best Practices

• Prefer containers by digest (not tags) and publish a manifest of GPU/driver/CUDA/cuDNN versions with your report.
• Warm up each model and run a small validation slice to test prompt templates, schema adherence, seeds, and logging before full runs.
• Use official scoring scripts and community harnesses (VLMEvalKit, LMMS‑Eval) to reduce handling drift; store raw predictions and per‑item metrics.
• Always run paired tests relative to a baseline (e.g., GLM‑Image); report both the delta and its confidence/adjusted significance.
• Randomize item order and distribute requests to reduce API rate-limit and diurnal effects; record region and streaming modes.
• Track invalid‑JSON rate and retries separately from task accuracy—format reliability is a first‑class metric for production integrations.
• Document any feature gaps (e.g., no boxes) as “not supported” rather than mixing incomparable modes; restrict RefCOCO comparisons to models that emit boxes.
• Publish everything: prompts, seeds, config files, container digests, harness versions, logs, and raw outputs. Anyone should be able to reproduce your numbers on another H100/A100 with identical software.

Practical Examples

Deterministic Docker + GPU sanity checks

# Pull by digest (example digest placeholder)
docker pull ubuntu@sha256:abcdef... # base image
# Record CUDA/cuDNN versions inside container
nvidia-smi # log driver, GPU UUID, power limit, MIG mode
# Disable MIG if enabled
sudo nvidia-smi -i 0 -mig 0 # requires admin; log result 

Normalized API request with JSON schema target and decoding

{
 "model": "gpt-4o-2024-xx-xx",
 "temperature": 0.2,
 "top_p": 0.9,
 "max_tokens": 128,
 "response_format": {"type": "json_object"},
 "messages": [
 {"role": "system", "content": "You are a helpful, precise vision-language assistant. Follow instructions exactly. If uncertain, say 'not sure'. Avoid unsupported claims."},
 {"role": "user", "content": [
 {"type": "input_text", "text": "Answer the VQA question with a single word."},
 {"type": "input_image", "image_url": "https://.../img1.jpg"},
 {"type": "input_text", "text": "Question: What color is the car?"}
 ]}
 ]
}

Where a provider exposes function/JSON modes, validate strict schemas and count invalid-JSON responses before retries. Include an Idempotency-Key header if the API supports it and log all headers.

Bootstrap confidence intervals with SciPy

from scipy.stats import bootstrap
import numpy as np

# scores: per-item metric (e.g., CIDEr) for a model on COCO captions
scores = np.array([...], dtype=float)
res = bootstrap((scores,), np.mean, confidence_level=0.95, n_resamples=10000, method="percentile")
mean = scores.mean()
lo, hi = res.confidence_interval.low, res.confidence_interval.high
print(f"Mean={mean:.3f} 95% CI=({lo:.3f}, {hi:.3f})") # 

Fixed frame sampling for short video (as multi-image)

def sample_frames(num_frames, k=16):
 # Evenly spaced indices inclusive of endpoints
 return [round(i*(num_frames-1)/(k-1)) for i in range(k)]

PyTorch seed and cuBLAS workspace (recap)

import os
os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":16:8" # deterministic GEMMs 
# Then import torch and set seeds as shown earlier 

Conclusion

Deterministic multimodal benchmarking isn’t a luxury—it’s the only way to make fair, defensible claims about GLM-Image versus GPT‑4o, Gemini, Claude, and Qwen2‑VL. By pinning model versions and endpoints, standardizing hardware and kernels, normalizing prompts/decoding/inputs, enforcing structured outputs, and applying bootstrap confidence intervals with paired significance tests, you eliminate confounders that otherwise masquerade as “model superiority.” Official benchmark scripts and harnesses close the loop, ensuring your numbers align with community baselines while remaining fully auditable.

Key takeaways:

• Fix versions, seeds, and containers; log everything that could change behavior.
• Normalize prompts, decoding, and inputs to keep comparisons apples‑to‑apples.
• Enforce JSON schemas and measure invalid‑JSON rates as a reliability metric.
• Use bootstrap CIs and paired tests (McNemar, permutation) with BH correction.
• Instrument latency/throughput (TTFT/TLTT) and run concurrency sweeps with warmed models.

Next steps: adopt the templates above, wire in official scoring for MMBench, MM‑Vet, MMMU, VQA v2, GQA, COCO, and RefCOCO, and publish your archive of prompts, seeds, digests, logs, and raw outputs. Do a cross‑day replication to validate stability, then iterate on task‑specific templates. With this rig in place, technical debates shift from anecdotes to evidence—and the best model for your workload becomes clear. 🔬

Sources

• ZhipuAI Open Platform (API): https://open.bigmodel.cn/dev/api — Documents model IDs/endpoints for GLM‑Image and provides the pinning surface.
• OpenAI Models (GPT-4o and others): https://platform.openai.com/docs/models — Specifies model naming/versioning and response features relevant to pinning and logging.
• OpenAI Vision Guide: https://platform.openai.com/docs/guides/vision — Describes multimodal request structure used in normalized prompts.
• OpenAI Function/Tool Calling: https://platform.openai.com/docs/guides/function-calling — Supports strict JSON/structured outputs used for schema adherence.
• Anthropic Claude Vision Docs: https://docs.anthropic.com/claude/docs/vision — Defines Claude’s vision input behavior used for comparable inputs/prompts.
• Google Gemini API Models: https://ai.google.dev/gemini-api/docs/models — Provides model identifiers and pinning surfaces for Gemini.
• Google Gemini Vision Guide: https://ai.google.dev/gemini-api/docs/vision — Details multimodal request formats informing input normalization.
• Qwen2‑VL GitHub: https://github.com/QwenLM/Qwen2-VL — Establishes model capabilities/limits and open-source configuration for pinning.
• MMBench (OpenCompass/MMBench): https://github.com/open-compass/MMBench — Official evaluation harness for broad multimodal reasoning.
• MM‑Vet Benchmark: https://mm-vet.github.io/ — Defines open-ended multimodal evaluation with rubric-based scoring.
• MMMU Benchmark: https://mmmu-benchmark.github.io/ — Multi‑discipline reasoning benchmark and official scoring.
• VQA v2 Dataset: https://visualqa.org/ — Standard VQA dataset and evaluation scripts for accuracy.
• GQA Dataset: https://cs.stanford.edu/people/dorarad/gqa/ — Compositional scene understanding with official evaluation.
• COCO Dataset (Captions): https://cocodataset.org/ — Captioning benchmark used with the official toolkit.
• COCO Caption Evaluation Toolkit: https://github.com/tylin/coco-caption — Official metric implementations (CIDEr/SPICE/BLEU/METEOR/ROUGE-L).
• RefCOCO/RefCOCO+/RefCOCOg (refer): https://github.com/lichengunc/refer — Official splits and IoU evaluation for grounding.
• VLMEvalKit: https://github.com/OpenGVLab/VLMEvalKit — Community harness to standardize dataset handling and scoring.
• OpenCompass Leaderboards (Multimodal): https://opencompass.org.cn/leaderboard — External sanity check for relative rankings.
• LMMS‑Eval: https://github.com/EvolvingLMMs-Lab/lmms-eval — Alternative harness for cross-validation of results.
• PyTorch Reproducibility/Randomness: https://pytorch.org/docs/stable/notes/randomness.html — Official guidance for deterministic training/inference.
• SciPy Bootstrap CI: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.bootstrap.html — Reference for non-parametric bootstrap CIs.
• NVIDIA A100: https://www.nvidia.com/en-us/data-center/a100/ — Hardware baseline and performance/VRAM characteristics.
• NVIDIA H100: https://www.nvidia.com/en-us/data-center/h100/ — Hardware baseline and performance/VRAM characteristics.
• Docker Docs: https://docs.docker.com/ — Containerization best practices and digest pinning.
• NVIDIA CUDA Docs: https://docs.nvidia.com/cuda/ — Kernel/library versioning and cuBLAS workspace determinism.

Sources & References