Grok 4.5 Benchmark Results vs GPT 5.5 & Claude Opus 4.8: Choosing a Model for Document Processing

The decision: shortlist Grok 4.5, then test it on your documents

For teams choosing an LLM for document processing, Grok 4.5 is a credible first model to evaluate. The supplied reporting from Snorkel AI says it demonstrated the strongest overall performance in its testing on GDPval+, a benchmark for training and evaluating AI models on professional work tasks. That result is relevant to contracts, policies, reports, applications, support records, and internal knowledge work—but it does not substitute for a task-specific production evaluation.

The practical choice is not “which model has the best benchmark headline?” It is: which model returns the required fields correctly, grounds them in the source, handles uncertainty safely, and fits the cost of your document volume?

This matters especially for a comparison framed as Grok 4.5 versus GPT 5.5 and Claude Opus 4.8. The supplied source summary supports the overall GDPval+ result for Grok 4.5, but does not provide scores or methodology details for a numeric comparison with GPT 5.5 or Claude Opus 4.8. Treat Grok’s result as a shortlist signal, then run a matched test rather than inferring a universal ranking.

Bottom line: For document operations, choose Grok 4.5 when it wins your evidence-backed extraction test at an acceptable cost. Keep Claude Opus 4.8 as a high-value comparator, and do not make a GPT 5.5 claim without equivalent, current results from your own evaluation.

What the reported Grok 4.5 result does—and does not—prove

Snorkel AI describes GDPval+ as a benchmark for professional work tasks and reports that Grok 4.5 demonstrated the strongest overall performance in its testing. That supports a measured conclusion: Grok 4.5 deserves attention for workflows involving professional documents and multi-step knowledge work.

Visible source: Snorkel AI: “Grok 4.5 Testing Results: How SpaceXAI’s New Model Performs on Real Professional Work”

It does not establish that Grok 4.5 will be best at every document task. A general professional-work benchmark may not match:

• your scan quality, layouts, tables, or OCR artifacts;
• the wording and schema of your agreements or intake forms;
• the languages and jurisdiction-specific terminology in your corpus;
• the required precision for dates, monetary values, entities, and clauses;
• your review rules when the document does not contain an answer.

A strong model can still fail in ways that matter operationally: extracting the right value from the wrong page, filling a missing field with a plausible guess, or producing a polished summary that loses a material exception. Those are evaluation problems, not merely prompting problems.

Grok 4.5 vs Claude Opus 4.8: the usable comparison for document teams

Both models are available through OneMux unified model routing. The known, comparable information in the supplied catalogue is pricing—not an equivalent benchmark scorecard. That makes cost an appropriate planning input, while quality must come from a controlled test.

For a workflow with input-heavy source documents, the input-price gap can materially affect recurring spend. Output cost matters most when prompts request lengthy explanations, clause-by-clause analysis, or full document rewrites. The better operating pattern is usually to request compact, structured outputs first, then generate a narrative only for records that need human review.

GPT 5.5 is named in the search intent for this comparison, but it is not part of the supplied OneMux model catalogue context and no GPT 5.5 pricing or evaluation result is provided here. Do not invent a ranking or cost comparison. If GPT 5.5 is part of your procurement process, include it in the same test harness only after confirming its current availability, model identifier, and price with the relevant provider.

A document-processing scorecard that prevents misleading wins

Evaluate models on a frozen set of real, permissioned documents. Split the set into development documents for prompt iteration and a held-out set that no one uses while tuning. Include straightforward documents and the cases that create manual work: amendments, inconsistent tables, duplicate names, incomplete files, multi-language documents, and low-quality scans.

Score extraction, evidence, and uncertainty separately

Use a schema that carries both answers and proof. For example, a contract workflow might require renewal date, governing law, payment terms, auto-renewal status, and the exact source text supporting each field.

A useful rubric has five dimensions:

1. Field accuracy: Is the normalized value correct?
2. Evidence fidelity: Does the cited quote actually support that value?
3. Completeness: Are all required fields returned when present?
4. Abstention quality: Does the model return not_found rather than guess when information is absent or unclear?
5. Operational cost: What are input tokens, output tokens, review time, and error-remediation cost per document?

Do not let fluent prose mask low extraction precision. A model that produces shorter, evidence-backed JSON and correctly flags ambiguity can be more valuable than one that writes a persuasive but unverifiable document summary.

Use a constrained output contract

Ask for a stable JSON structure, permitted status values, and source evidence. Preserve the original document text and model output for reviewers. Validate JSON in application code, and route invalid records or low-confidence cases to a human queue.

from openai import OpenAI
import json
import os

client = OpenAI(
    api_key=os.environ["ONEMUX_API_KEY"],
    base_url=os.environ["ONEMUX_BASE_URL"],
)

schema = {
    "document_id": "contract-1042",
    "fields": [
        {
            "name": "renewal_date",
            "value": None,
            "status": "found | not_found | ambiguous",
            "evidence": "exact supporting text"
        }
    ]
}

response = client.chat.completions.create(
    model=os.environ["DOCUMENT_MODEL"],
    messages=[
        {
            "role": "system",
            "content": "Extract only supported facts. Return valid JSON matching the requested shape. Use not_found when absent and ambiguous when the text conflicts or is unclear."
        },
        {
            "role": "user",
            "content": f"Schema example:\n{json.dumps(schema)}\n\nDocument:\n{os.environ['DOCUMENT_TEXT']}"
        }
    ]
)

print(response.choices[0].message.content)

Use the model identifier and endpoint configuration documented for your OneMux account. The point of the example is the evaluation contract: force an explicit distinction between found, absent, and ambiguous information.

Design the workflow by team, not just by model

Developers should build document ingestion, OCR or text extraction where needed, schema validation, retry logic, and reviewer queues outside the model prompt. Founders should measure unit economics against the portion of manual handling the workflow actually removes. Operators need exception dashboards: which document types fail, which fields are most often ambiguous, and how often reviewers override the output.

For marketers and support teams, the same rule applies to briefs, campaign reports, tickets, and account notes: preserve source excerpts for claims that will reach customers. International buyers should test the languages, date formats, currencies, and local terms that occur in their own files rather than relying on an English-centric sample.

OneMux is useful when you want to run that comparison without building separate API integrations. It provides access to leading AI models through one OpenAI-compatible API, with model access, routing, keys, spend visibility, credit top-ups, and pay-as-you-go usage in one place. Start with the OneMux quickstart, consult the API documentation, and review pricing before setting volume assumptions.

Conclusion

Grok 4.5’s reported strength on GDPval+ is meaningful evidence for professional document work, and its listed OneMux pricing makes it a sensible model to place early in an evaluation. But the best model for document processing is the one that proves itself on your files: correct normalized fields, source-grounded evidence, safe handling of uncertainty, and a total operating cost that survives real volume.

Run Grok 4.5 and Claude Opus 4.8 through the same held-out corpus and scoring rules. Add GPT 5.5 only when you have verified current access and can test it under the same conditions. That produces a decision your engineering, operations, and finance teams can defend.

FAQ

Does the reported GDPval+ result mean Grok 4.5 is the best model for every document workflow?

No. The supplied source supports that Grok 4.5 performed strongest overall in Snorkel AI’s GDPval+ testing. It does not prove superiority for every document type, language, extraction schema, or production requirement.

Is Grok 4.5 less expensive than Claude Opus 4.8 on OneMux?

Based on the supplied catalogue prices, yes. Grok 4.5 is listed at $0.85 per 1M input tokens and $2.50 per 1M output tokens. Claude Opus 4.8 is listed at $2.70 input and $13.50 output per 1M tokens. Actual workflow cost depends on document length and output volume.

What should a document-processing pilot measure?

Measure field accuracy, evidence fidelity, completeness, correct abstention, invalid-output rate, review time, and token consumption. Keep a held-out test set so prompt changes do not create an overly optimistic result.

Can one integration be used to test more than one model?

OneMux provides access to leading AI models through one OpenAI-compatible API. Review the OneMux model catalogue and documentation for the models and configuration available to your account.

Sources

• Snorkel AI, “Grok 4.5 Testing Results: How SpaceXAI’s New Model Performs on Real Professional Work”
• OneMux Grok 4.5 model page
• OneMux Claude Opus 4.8 model page