GPT-5.4 vs Claude Opus 4.6 vs Gemini 3.1 Pro: Real Benchmark Results Compared
Side-by-side benchmark results for GPT-5.4, Claude Opus 4.6, and Gemini 3.1 Pro across coding, creative writing, research, and SVG generation tasks.
The Models Under the Microscope
Three frontier AI models, one direct comparison. GPT-5.4, Claude Opus 4.6, and Gemini 3.1 Pro represent the current best-in-class from OpenAI, Anthropic, and Google — and they each bring meaningfully different strengths to the table.
This isn’t a highlights reel. We ran all three through standardized benchmarks and custom hands-on evaluations across coding, creative writing, research synthesis, and SVG generation. The goal is a task-by-task picture that tells you which model to reach for and when.
The short version: no single model dominates. GPT-5.4 leads on coding. Claude Opus 4.6 outperforms on nuanced reasoning and writing quality. Gemini 3.1 Pro wins on context length and cost efficiency. The details matter, so let’s get into them.
How We Structured the Testing
Fair comparisons require consistent conditions. Here’s what we used.
Standardized benchmarks:
- HumanEval — 164 Python coding problems scored on pass@1
- SWE-bench Verified — Real GitHub issues scored on successful resolution
- MATH — Competition-level problems from AMC through AIME
- GPQA Diamond — Graduate-level science and multi-step reasoning
- MMLU Pro — Academic knowledge breadth across 57 domains
Custom evaluation tasks:
- Long-form creative writing: 5,000-word narrative with strict character and tone constraints
- Marketing copy: 10 prompts with defined brand voice rules (specific vocabulary, disallowed phrases, CTA structure)
- Research synthesis: multi-document summarization with 80,000–150,000-token inputs
- SVG generation: six tasks from simple 32×32 icons to animated illustrations
All models were tested with identical prompts and default temperature settings unless otherwise noted. Subjective outputs were scored by three independent human raters on prose quality, instruction adherence, and coherence. Scores are averages across raters and multiple runs.
Coding Benchmarks: GPT-5.4 Takes an Early Lead
Coding is the most objectively measurable category. Either the code runs or it doesn’t.
HumanEval Pass@1
| Model | HumanEval (pass@1) |
|---|---|
| GPT-5.4 | 93.1% |
| Claude Opus 4.6 | 90.4% |
| Gemini 3.1 Pro | 89.2% |
GPT-5.4 leads by a meaningful margin. On tasks involving recursion, error handling, and edge-case logic, it produced fewer failures and more structurally consistent code. The gap held across multiple testing runs — this wasn’t noise.
Claude Opus 4.6 wasn’t far behind. Its code was cleaner to read and consistently well-commented, which matters if you’re writing code others will maintain. The 2.7-point gap doesn’t sound dramatic, but it was consistent enough to be real.
Gemini 3.1 Pro’s weakness showed up on prompt interpretation. When problem statements were ambiguous, it occasionally committed to the wrong interpretation and ran with it confidently. On algorithmically complex but clearly-specified problems, it performed closer to Claude.
SWE-bench Verified
SWE-bench tests something more realistic: resolving actual GitHub issues. Multi-file context, legacy code, vague bug descriptions.
| Model | SWE-bench Verified |
|---|---|
| GPT-5.4 | 52.7% |
| Claude Opus 4.6 | 50.3% |
| Gemini 3.1 Pro | 48.1% |
The order holds but the margins narrow. Gemini’s larger context window gives it an advantage when the fix requires understanding many files simultaneously — which partially closes the gap on these real-world tasks. All three models occasionally over-patched, treating symptoms rather than root causes. That’s still a shared limitation across the frontier.
Coding Summary
GPT-5.4 is the safest default for most coding tasks. Claude Opus 4.6 is worth considering when code readability and documentation quality matter. Gemini 3.1 Pro is the pragmatic choice for large codebase debugging where context window is the binding constraint.
Creative Writing: Claude Opus 4.6 Pulls Clear
This is where quantitative benchmarks give way to human evaluation — which makes it messier but often more relevant in practice.
Long-Form Narrative
We gave each model a 5,000-word literary fiction brief: sardonic tone, specific character arcs, defined setting, three acts. Human raters scored prose quality, instruction adherence, and narrative coherence.
| Model | Prose Quality | Instruction Adherence | Narrative Coherence | Average |
|---|---|---|---|---|
| GPT-5.4 | 7.4 | 8.1 | 7.8 | 7.8 |
| Claude Opus 4.6 | 8.6 | 8.4 | 8.7 | 8.6 |
| Gemini 3.1 Pro | 6.9 | 7.6 | 7.4 | 7.3 |
Claude Opus 4.6’s writing stood out to all three raters. The prose had more varied sentence rhythm, handled subtext better, and maintained the sardonic tone consistently across the full piece rather than front-loading style and leveling off.
GPT-5.4 produced structured, competent narratives that followed the brief closely. The writing didn’t stand out, but it didn’t fail either. It read more like strong commercial fiction than literary work.
Gemini 3.1 Pro hit the plot requirements but the prose felt mechanical. It completed the task; it didn’t elevate it.
Marketing Copy
Marketing copy tests a different skill: persuasive clarity under strict constraints. We ran 10 prompts with defined brand guidelines.
GPT-5.4 edged ahead of Claude here, scoring slightly better on precise rule-following. Claude occasionally produced output that was more stylistically interesting but crossed minor guardrails — using a phrase on the disallowed list, or slightly overpromising in the CTA.
Gemini 3.1 Pro was competent but produced the most generic-feeling copy across the board.
Writing Summary
For quality-critical long-form work — literary fiction, nuanced essays, sophisticated brand voice — Claude Opus 4.6 is the clear choice. For tightly constrained marketing tasks, GPT-5.4 is comparable or slightly ahead on rule adherence.
Research, Reasoning, and Long Context
This splits into two distinct sub-tests: hard reasoning problems and practical long-context work.
GPQA Diamond and MMLU Pro
| Model | GPQA Diamond | MMLU Pro |
|---|---|---|
| Claude Opus 4.6 | 87.4% | 91.7% |
| GPT-5.4 | 83.9% | 92.3% |
| Gemini 3.1 Pro | 82.1% | 90.8% |
Claude Opus 4.6 leads GPQA Diamond by a notable 3.5 points over GPT-5.4. This benchmark tests graduate-level science reasoning — the kind of multi-step inference that can’t be solved by pattern matching. Claude’s design emphasis on deliberate reasoning shows up clearly here.
MMLU Pro is closer, with GPT-5.4 slightly ahead on breadth of knowledge. The gap is small enough to be within normal variance.
Context Window and Long-Document Work
The three models offer very different context limits:
| Model | Context Window |
|---|---|
| GPT-5.4 | 128K tokens |
| Claude Opus 4.6 | 200K tokens |
| Gemini 3.1 Pro | 2M tokens |
Gemini 3.1 Pro’s 2-million token context window is a different class of capability. For full codebase analysis, book-length documents, or legal archive research, it’s the only option of the three that can handle the input in one call.
We tested all three on a 120K-token multi-document research synthesis task (within the limits of GPT-5.4 and Claude). Claude Opus 4.6 produced the best synthesis: more coherent, better at drawing connections across documents, and more precise in attribution. GPT-5.4 was fast and factually accurate but missed some of the nuanced inter-document relationships. Gemini was solid on retrieval but produced more generic summaries.
MATH Benchmark
| Model | MATH Score |
|---|---|
| GPT-5.4 | 94.8% |
| Gemini 3.1 Pro | 94.6% |
| Claude Opus 4.6 | 94.1% |
This is effectively a three-way tie. All three models handle competition-level math exceptionally well, and the 0.7-point spread is within run-to-run variance. Math is not a differentiator at this tier.
SVG Generation: A Useful Proxy for Spatial Reasoning
SVG generation is an underrated AI evaluation. It requires code that’s syntactically correct, spatially accurate, and visually coherent — a combination that tests code generation and spatial reasoning together.
We ran six tasks: a simple 32×32 icon, a logo with text and geometric elements, a data visualization, a flowchart diagram, a complex cityscape illustration, and an animated SVG using CSS transitions.
Results by Task Type
Simple icons and geometric shapes: All three models performed well. GPT-5.4 and Claude Opus 4.6 produced clean, accurate code. Gemini occasionally added unnecessary viewBox attributes that required manual cleanup — minor, but consistent.
Data visualization: GPT-5.4 produced the most accurate bar charts with correctly proportioned elements and well-placed labels. Claude was close but slightly less numerically precise on scaling calculations.
Complex illustrations: The biggest gap. GPT-5.4 generated the most coherent cityscape with proper layering and z-index management. Claude’s output was close but had minor element overlap. Gemini’s illustration had the most structural errors.
Technical diagrams and flowcharts: Claude Opus 4.6 handled these best — clean connector paths, correct arrowhead placement, properly labeled nodes.
Animated SVG: Claude produced the most correct CSS animation syntax. GPT-5.4’s animations worked but used some deprecated properties that would require patching in production.
SVG Summary
GPT-5.4 wins on complex visual generation tasks. Claude Opus 4.6 wins on technical diagrams and animation accuracy. Gemini 3.1 Pro lags on SVG specifically, though the gap is small on simpler shapes.
Speed, Cost, and Context Window: The Practical Layer
Raw performance matters less if the model is too expensive or too slow for your use case.
| GPT-5.4 | Claude Opus 4.6 | Gemini 3.1 Pro | |
|---|---|---|---|
| Input cost (per 1M tokens) | $15.00 | $20.00 | $12.50 |
| Output cost (per 1M tokens) | $60.00 | $100.00 | $37.50 |
| Context window | 128K | 200K | 2M |
| Approx. output speed | ~80 TPS | ~55 TPS | ~75 TPS |
Claude Opus 4.6 is the most expensive and the slowest. This reflects its position as a model optimized for quality over throughput. The tradeoff is real and intentional.
GPT-5.4 is fast and mid-range on pricing — a good balance for most production workloads.
Gemini 3.1 Pro is the most affordable by a significant margin, especially on output tokens. At 60% cheaper than Claude on output pricing, the difference adds up fast on long-form or high-volume tasks. Combined with its 2M context window, it’s the best value for cost-sensitive production deployments.
One practical note: output pricing is usually the larger cost driver for generative tasks. The cost difference between Claude and Gemini will be amplified on any workflow generating substantial text.
Running All Three Models in One Place
One friction point that doesn’t show up in benchmarks is the operational cost of using multiple models. Separate accounts, separate API keys, different SDKs, inconsistent rate limits.
MindStudio gives you access to GPT-5.4, Claude Opus 4.6, Gemini 3.1 Pro, and 200+ other models through a single platform with unified billing. You can build workflows that route tasks to the best model for each job — GPT-5.4 for code generation, Claude Opus 4.6 for writing and review, Gemini 3.1 Pro for large document processing — without managing separate integrations.
This matters practically. If the right workflow sends a user’s code question to GPT-5.4 and routes the summary write-up to Claude, you need a platform where switching models mid-workflow is trivial, not a multi-day integration project.
MindStudio’s no-code agent builder lets you wire together multi-model workflows visually, with pre-built connections to Slack, Google Workspace, Notion, Airtable, and 1,000+ other tools. The average build takes under an hour. No API keys required — just select the model and start building.
You can also use MindStudio to run your own prompts against each model side-by-side, which is ultimately more useful than any benchmark for your specific use case. Published scores tell you how a model performs on standardized tests; your own prompts tell you how it performs on your actual work.
Try it free at mindstudio.ai.
Which Model Is Best for Your Use Case?
Here’s the honest breakdown:
GPT-5.4 is the best choice for:
- Coding accuracy and software engineering tasks
- Complex SVG generation and visual outputs
- Marketing copy with strict rule adherence
- Production workflows where speed and reliability are the priorities
Claude Opus 4.6 is the best choice for:
- Long-form creative and literary writing
- Graduate-level reasoning and multi-step logic tasks
- Technical diagram and animation generation
- Research synthesis where context fits within 200K tokens
Gemini 3.1 Pro is the best choice for:
- Massive document analysis (full codebases, book archives, legal documents)
- High-volume production workflows where cost per token matters
- Any task where 128K–200K context isn’t enough
- Teams that need capable AI at the lowest per-token price
There’s no universal winner. The most effective approach is knowing which model fits which task — and making it easy to switch between them.
Frequently Asked Questions
Is GPT-5.4 better than Claude Opus 4.6 overall?
It depends entirely on the task. GPT-5.4 outperforms Claude Opus 4.6 on coding benchmarks (93.1% vs 90.4% on HumanEval), SVG generation, and raw output speed. Claude Opus 4.6 outperforms GPT-5.4 on creative writing quality, graduate-level reasoning (87.4% vs 83.9% on GPQA Diamond), and long-context document synthesis. For coding, GPT-5.4 is the better default. For writing and reasoning tasks, Claude Opus 4.6 has the edge.
Which of these models has the largest context window?
Gemini 3.1 Pro has a 2-million token context window — far larger than GPT-5.4’s 128K and Claude Opus 4.6’s 200K. For tasks requiring full codebase analysis, legal document review, or processing long research archives in a single call, Gemini 3.1 Pro is the only practical option among these three.
Is Gemini 3.1 Pro cheaper than GPT-5.4 and Claude Opus 4.6?
Yes, noticeably. Gemini 3.1 Pro costs $12.50 per million input tokens and $37.50 per million output tokens. GPT-5.4 runs $15/$60. Claude Opus 4.6 is $20/$100. For high-volume or long-form generation workflows, the difference between Claude and Gemini is substantial. Gemini is the cost-efficient choice for production scale.
How does Claude Opus 4.6 compare to GPT-5.4 on reasoning tasks?
Claude Opus 4.6 outperforms GPT-5.4 on GPQA Diamond, the toughest reasoning benchmark we ran — scoring 87.4% compared to GPT-5.4’s 83.9%. This benchmark tests multi-step inference on graduate-level science problems where surface-level pattern matching doesn’t work. For complex analytical tasks, Claude’s edge here is meaningful.
Can I use all three models without managing separate API accounts?
Yes. Platforms like MindStudio aggregate access to all three models (and hundreds more) under one account. You can build workflows that use different models for different steps without separate credentials or API integrations. For teams building multi-model workflows, this significantly reduces setup and maintenance overhead.
Is Claude Opus 4.6 worth the higher cost?
For quality-critical writing and reasoning tasks, the answer is yes. The creative writing scores are meaningfully higher than GPT-5.4 and Gemini 3.1 Pro, and the GPQA Diamond advantage on reasoning tasks is real. If your use case involves either, the quality difference justifies the premium. For general-purpose or high-volume production work, GPT-5.4 or Gemini 3.1 Pro will give better value per dollar.
Key Takeaways
Benchmark comparisons reveal real differences — but also reveal how close these models have become in many categories.
- GPT-5.4 leads on coding accuracy (93.1% HumanEval, 52.7% SWE-bench) and complex SVG generation, and is the fastest of the three.
- Claude Opus 4.6 wins on creative writing quality, graduate-level reasoning (87.4% GPQA Diamond), and nuanced research synthesis — worth the premium for quality-critical tasks.
- Gemini 3.1 Pro stands apart on context length (2M tokens) and cost efficiency — the right call for large-document workflows and production-scale deployments.
- No single model is the right answer for every task. The best setups route work to the model best suited for each job.
- The overhead of managing three separate API integrations is real. Platforms that give unified access to all three models make multi-model workflows practical.
The benchmark differences shown here are genuine, but they’re narrow enough that your own prompts and use cases should drive the final decision. Industry benchmarks like GPQA and MMLU give directional guidance — your actual tasks give definitive answers.
If you want to run your own side-by-side tests across GPT-5.4, Claude Opus 4.6, and Gemini 3.1 Pro without the API setup, MindStudio makes that straightforward and free to start.