Claude vs. ChatGPT for Immigration Attorneys: EB-1A Output

The standard ChatGPT vs. Claude comparison covers pricing, features, and use cases in the abstract. None of them run the comparison on actual EB-1A drafting tasks and show you what each model produces.

That is what this article does. Four tasks: a Criterion 5 argument paragraph, an expert letter opening, a Kazarian Step 2 challenge response, and a high-salary evidence section. Same input, both models, with annotations on what works and what does not.

A caveat before the outputs: these are representative examples based on documented model behavior patterns. Actual output varies by model version, prompt wording, and context. Use these to calibrate expectations, not as predictions of what your specific prompt will produce.

How the Comparison Works

The same input was used for each task:

• Model: Claude Opus 4.6 vs. ChatGPT GPT-4o
• System prompt: The EB-1A legal context system prompt from the AI prompts guide
• Task input: Identical for both models
• Evaluation dimensions: Legal framing quality, specificity, hallucination tendency, adherence to the EB-1A regulatory standard, output length

For tool tier setup, confidentiality requirements, and BAA comparison, see Best AI Tools for Immigration Attorneys 2026. Both models are evaluated against the EB-1A adjudication standard in the USCIS Policy Manual, Vol. 6, Part F, Chapter 2.

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Task 1: Criterion 5 Argument Paragraph

Input given to both models:

Draft the Criterion 5 argument section for [BENEFICIARY]'s EB-1A petition.

Evidence: Published a training efficiency method for large language models (2022). 214 independent citations in 24 months. Three subsequent papers explicitly built on the method. Expert letter from Professor Chen (independent, different institution) states the method reduced computational costs by approximately 40% and changed methodological practice in the subfield.

Exhibit references: Exhibit 7 (citation analysis), Exhibit 8 (paper), Exhibit 9 (Professor Chen letter)

Length: 350-400 words.

Claude output (representative):

Under 8 CFR 204.5(h)(3)(v), [BENEFICIARY] has made original contributions of major significance to the field of large language model training efficiency. The evidence demonstrates that the contribution has influenced how independent researchers in this subfield approach their work — the operative test for field-level significance.

[BENEFICIARY]'s 2022 paper (Exhibit 8) introduced a training efficiency method that reduced computational costs by approximately 40%, as documented by Professor Chen (Exhibit 9), an independent expert at a different institution who has cited the work in her own research. This reduction was not incremental. Prior to this method, researchers at institutions without industrial-scale GPU access were constrained in their ability to train models at the scales necessary for competitive research results. The method removed this constraint for a meaningful portion of the active researcher population in this subfield.

The field-level significance of this contribution is evidenced by adoption, not merely by citation count. Three independent research groups explicitly built on the method in their own published systems (Exhibits 10–12), incorporating it as a methodological component rather than citing it as related work. This is the distinction USCIS officers should look for: citation as acknowledgment differs from adoption as foundation. The 214 independent citations in the 24 months following publication (Exhibit 7) include citations from researchers who went on to adopt the method, not merely acknowledge it.

Professor Chen states in her letter (Exhibit 9) that this contribution "changed the methodological practice" in the subfield of LLM training efficiency. She is positioned to make this assessment as an independent researcher who encountered the work through the published literature and independently verified its applicability to her own experiments. Her letter is not a character reference. It is a technical assessment by an expert in the relevant subfield.

ChatGPT output (representative):

[BENEFICIARY] satisfies Criterion 5 through the development and publication of a training efficiency method for large language models that has generated significant field-level impact since its 2022 publication.

The method, documented in Exhibit 8, reduced computational costs by approximately 40% according to independent expert Professor Chen (Exhibit 9). Professor Chen's assessment reflects her independent evaluation as a researcher at a different institution who encountered and applied the method in her own work.

The contribution has received 214 independent citations within 24 months of publication (Exhibit 7). More significantly, three subsequent research groups explicitly built on the method in their own published systems (Exhibits 10–12), demonstrating adoption rather than mere acknowledgment. This adoption pattern distinguishes the work as a contribution that changed research practice, not merely contributed to the literature.

The combination of independent citations, explicit methodological adoption, and expert testimony from Professor Chen establishes that [BENEFICIARY]'s contribution has influenced the field beyond [his/her] individual work — the standard required under 8 CFR 204.5(h)(3)(v) for original contributions of major significance.

Assessment:

Both outputs are usable. Claude's version is stronger on two points: it explicitly frames the before/after state of the field ("prior to this method, researchers... were constrained"), and it draws the citation-versus-adoption distinction that USCIS officers have noted in AAO decisions. ChatGPT's version is more compact and follows the requested length more precisely, but it omits the comparative framing that distinguishes a major contribution from a good one.

Winner for this task: Claude. The Step 2 framing and before/after field analysis require less attorney editing.

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Task 2: Expert Letter Opening Paragraph

Input:

Draft the opening paragraph of an expert letter for [BENEFICIARY]'s EB-1A petition. Expert: Professor Chen, associate professor of computer science at [Institution B], independent of the beneficiary. She has cited the beneficiary's work in her own research. Criteria addressed: C5.

Claude output:

I am an associate professor of computer science at [Institution B], where my research focuses on large language model training methods and computational efficiency. I have no employment, supervisory, or prior collaborative relationship with [BENEFICIARY]; my familiarity with [his/her] work comes through the published literature and my own evaluation of its applicability to my research program.

ChatGPT output:

I write to support [BENEFICIARY]'s petition for EB-1A classification as a person of extraordinary ability. I am Professor Chen, an associate professor of computer science at [Institution B] specializing in machine learning systems. I have followed [BENEFICIARY]'s research through the published literature and have independently incorporated [his/her] methods in my own work.

Assessment:

Claude's version establishes independence more directly: it names the absence of an employment or supervisory relationship, which is the specific independence standard USCIS applies. ChatGPT's version leads with the support purpose rather than the expert's credential and independence. Both are one edit away from good. But Claude's version is closer to correct in structure without prompting.

Winner for this task: Claude, by a narrower margin.

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Task 3: RFE Step 2 Response Paragraph

Input:

Draft a 250-word response to the following RFE language: "The petitioner has not demonstrated through a final merits determination that the beneficiary, considered in totality, has achieved the level of acclaim and recognition commensurate with national or international acclaim."

Available evidence: citation percentile (top 8% of ML researchers publishing in top venues over same period), salary at 97th percentile for the field, multiple awards listed, expert letters from independent experts.

Claude output:

The record, considered in totality, establishes that [BENEFICIARY] has risen to the very top of the field of LLM training efficiency research. The officer's concern appears to rest on evidence evaluated individually rather than cumulatively, which does not reflect the totality analysis the Kazarian framework requires.

[BENEFICIARY]'s citation percentile of the top 8% among machine learning researchers publishing in top venues over the comparable period (Exhibit 7) locates [him/her] within the narrow band of researchers whose work is actively built upon by others in the field. This is not a middling researcher who has published in reputable venues. It is a researcher whose output the research community treats as foundational.

At the 97th salary percentile for the field (Exhibit 15), [BENEFICIARY]'s compensation reflects market valuation of the specific expertise [he/she] provides. The combination of citation impact, salary, and expert testimony from multiple independent researchers who have engaged with the work in their own research programs constitutes a totality of evidence placing [BENEFICIARY] above the broad middle of the field and into the category of those who have achieved sustained national acclaim in the sense USCIS applies under the Kazarian framework.

ChatGPT output:

[BENEFICIARY]'s record, viewed in totality, establishes national acclaim at the highest level in the field of LLM training efficiency research. The combination of citation impact (top 8th percentile among ML researchers in comparable venues), compensation at the 97th field percentile, recognition through multiple awards, and expert testimony from independent researchers collectively demonstrates that [BENEFICIARY] occupies the upper tier of this competitive subfield.

The final merits determination requires examining whether the record, as a whole, reflects the extraordinary standard. It does. Each criterion established in Step 1 represents an independent data point. Taken together, they describe a researcher whose work is cited by peers, compensated above 97% of the field, and recognized through multiple award mechanisms. This totality places [BENEFICIARY] well above the broad middle and into the narrow upper tier that qualifies as national acclaim.

Assessment:

ChatGPT's version is cleaner and more compact. Claude's version makes a stronger legal argument (specifically addressing why the officer's criterion-by-criterion analysis misapplies Kazarian), but its length may exceed the 250-word instruction. For this task, ChatGPT follows the length constraint more precisely while producing a defensible argument.

Winner for this task: Tie, with a slight edge to ChatGPT for instruction adherence.

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Task 4: High-Salary Evidence Section

Input:

Draft a Criterion 9 argument for [BENEFICIARY]. Evidence: base salary $342,850 at the 97th percentile for ML engineers nationally (OES data, all industries). 

Claude output:

[BENEFICIARY]'s annual base salary of $342,850 establishes high remuneration relative to others in the field under 8 CFR 204.5(h)(3)(ix). According to the Bureau of Labor Statistics Occupational Employment and Wage Statistics (OES) survey data (Exhibit 14), this figure places [BENEFICIARY] at the 97th percentile for machine learning engineers across all industries nationally. Ninety-seven percent of similarly classified workers in the field earn less than [BENEFICIARY]. This is not a high salary within a specific employer or market; it is a high salary by the national field standard that the criterion requires.

ChatGPT output:

[BENEFICIARY] satisfies Criterion 9 through a base salary of $342,850, which represents the 97th percentile of nationally reported wages for ML engineers (Bureau of Labor Statistics OES data, Exhibit 14). This compensation places [BENEFICIARY] above 97% of field peers, establishing high remuneration relative to others in the field as required by 8 CFR 204.5(h)(3)(ix).

Assessment:

For this task, ChatGPT produces a cleaner, more direct argument. The final sentence in Claude's version ("This is not a high salary within a specific employer or market; it is a high salary by the national field standard") is useful attorney reasoning but belongs in the cover letter brief, not the petition argument itself. Both outputs correctly anchor the claim to Bureau of Labor Statistics Occupational Employment and Wage Statistics data rather than employer-specific compensation, which is the right comparator for Criterion 9. ChatGPT's output requires less trimming.

Winner for this task: ChatGPT.

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Summary

The table below summarizes the verdict across all four tasks tested in this comparison.

Task	Claude	ChatGPT	Edge
Criterion 5 argument	More complete before/after framing, citation/adoption distinction	More compact, better instruction adherence	Claude
Expert letter opening	Independence framing is structurally correct by default	Purpose-first structure, needs minor reordering	Claude
RFE Step 2 response	More complete Kazarian comparative framing	Cleaner, better length compliance	Tie
High-salary section	Adds useful but verbose attorney reasoning	Cleaner, directly USCIS-appropriate	ChatGPT

The practical workflow most attorneys settle on: use Claude (Sonnet or Opus) for Criterion argument sections, expert letter briefs, and Kazarian Step 2 drafts. Use either model for shorter structured tasks where explicit formatting matters more than sustained legal reasoning. Run both on any task where you are unsure which produces less editing work.

For the complete prompt library that works with both models, see 30 AI Prompts for Immigration Attorneys. For confidentiality configuration before using either model with client data, see AI Confidentiality for Immigration Attorneys. For the full tool tier and BAA comparison, see Best AI Tools for Immigration Attorneys 2026.

Immigration Copilot runs on Claude Opus via Amazon Bedrock with your client's full exhibit file loaded as context. The model has access to every uploaded document, which eliminates the bracket-replacement step and grounds every argument in actual exhibit citations.