GPT-5.4 and Gemini 3.1 Pro are one point apart on BenchLM's leaderboard. We compare coding, math, reasoning, multimodal, agentic, pricing, and context window — with Gemini Deep Think as the reasoning wildcard.
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GPT-5.4 and Gemini 3.1 Pro are separated by a single point on BenchLM's overall leaderboard — 84 to 83. But the score hides a deeper story: these models represent fundamentally different bets on what frontier AI should be. OpenAI is building a reasoning-first agent OS. Google is building a natively multimodal platform and pricing it to win volume. And with Gemini 3 Pro Deep Think, Google now has a reasoning specialist that matches GPT-5.4 on the hardest problems while offering a 2M-token context window.
Here's how they actually compare.
| Category | GPT-5.4 | Gemini 3.1 Pro | Deep Think | Winner |
|---|---|---|---|---|
| Overall Score | 84 | 83 | 79 | GPT-5.4 (by 1 point) |
| Type | Reasoning | Non-Reasoning | Reasoning | — |
| Context Window | 1.05M | 1M | 2M | Deep Think |
| SWE-bench Verified | 84 | 75 | 58 | GPT-5.4 |
| SWE-Pro | 57.7 | 72 | 63 | Gemini 3.1 Pro |
| AIME 2025 | 99 | — | 98 | GPT-5.4 / Deep Think |
| MATH-500 | 99 | 97 | 92 | GPT-5.4 |
| GPQA Diamond | 92.8 | 94.3 | 97 | Deep Think |
| MuSR | 94 | 93 | 93 | GPT-5.4 |
| LongBench v2 | — | 93 | 94 | Deep Think |
| MRCRv2 | 97 | 90 | 96 | GPT-5.4 |
| ARC-AGI-2 | 73.3 | 77.1 | 45.1 | Gemini 3.1 Pro |
| BrowseComp | 82.7 | 86 | 87 | Deep Think |
| OSWorld | 75 | 68 | 73 | GPT-5.4 |
| MMMU-Pro | 81.2 | 83.9 | 95 | Deep Think |
| Price (in/out per 1M) | $2.50 / $15 | $1.25 / $5 | TBD | Gemini 3.1 Pro |
No model sweeps the table. GPT-5.4 wins on math, factual recall, and desktop agents. Gemini 3.1 Pro wins on multimodal, real-world coding (SWE-Pro), and price. Deep Think wins the hardest reasoning benchmarks but trails on practical tasks.
| Benchmark | GPT-5.4 | Gemini 3.1 Pro | Deep Think |
|---|---|---|---|
| SWE-bench Verified | 84 | 75 | 58 |
| SWE-bench Pro | 57.7 | 72 | 63 |
| LiveCodeBench | 84 | 71 | 58 |
| TerminalBench 2.0 | 75.1 | 77 | 77 |
| SciCode | 52.5 | 59 | — |
| HumanEval | 95 | 91 | 91 |
GPT-5.4 absorbed the Codex line starting with version 5.4 — there is no separate GPT-5.4-Codex. This gives it a unified model for long-horizon engineering: PRDs, code transforms, deploys, monitoring. On clean, well-scoped repo tasks (SWE-bench Verified, LiveCodeBench), it leads convincingly.
But SWE-Pro tells a different story. This benchmark uses messier, more realistic codebases — and Gemini 3.1 Pro leads it 72 to 57.7. JetBrains reported up to 15% improvement in Gemini 3.1 Pro over prior previews, with notably better token efficiency. Gemini also edges ahead on TerminalBench 2.0 (77 vs 75.1) and SciCode (59 vs 52.5).
The pattern: GPT-5.4 excels at synthetic-clean coding tasks. Gemini handles the mess of real-world software better. If you are building with Codex-style autonomous workflows, GPT-5.4 is still the default. For cost-conscious pair programming where the codebase is not perfectly structured, Gemini is increasingly hard to ignore.
Full coding rankings: Best LLMs for Coding.
| Benchmark | GPT-5.4 | Gemini 3.1 Pro | Deep Think |
|---|---|---|---|
| AIME 2025 | 99 | — | 98 |
| HMMT 2025 | 97 | — | 96 |
| USAMO 2026 | 95.2 | 74.4 | — |
| MATH-500 | 99 | 97 | 92 |
| Frontier Math | 47.6 | 36.9 | — |
| GPQA Diamond | 92.8 | 94.3 | 97 |
| BBH | 97 | 92 | 95 |
| ARC-AGI-2 | 73.3 | 77.1 | 45.1 |
GPT-5.4 is the math king. It scores 99 across every AIME year (2023–2025), 95.2 on USAMO 2026, and 47.6 on Frontier Math — the hardest math benchmark available. Gemini 3.1 Pro does not even have AIME or HMMT results; it simply was not designed for competition math.
Deep Think changes the picture. It scores 98–99 on AIME, achieved gold-medal performance on IMO 2025, and hits 97 on GPQA Diamond — beating both GPT-5.4 (92.8) and Gemini 3.1 Pro (94.3). Google built Deep Think specifically for scientific discovery and research-grade problems, and it delivers.
But here is what makes this interesting: Gemini 3.1 Pro, a non-reasoning model, scores 83 overall compared to GPT-5.4's 84 — a reasoning model. A January 2026 paper on "Societies of Thought" found that reasoning gains come from internally simulating diverse cognitive perspectives, not just longer chains of thought. Anthropic's own research showed reasoning models do not always faithfully report their actual process. The debate has moved on from "does reasoning work?" to "when is reasoning worth the latency cost?"
For daily work, GPT-5.4's reasoning overhead may not justify its edge. For PhD-level research, competition math, or scientific problems — GPT-5.4 or Deep Think are in a class of their own.
| Benchmark | GPT-5.4 | Gemini 3.1 Pro | Deep Think |
|---|---|---|---|
| MMMU-Pro | 81.2 | 83.9 | 95 |
| OfficeQA-Pro | 96 | 95 | 95 |
| SimpleVQA | 61.1 | 72.4 | — |
| CharXiv | 82.8 | 80.2 | — |
| MedXpert-QA (MM) | 77.1 | 81.3 | — |
| ScreenSpot-Pro | 85.4 | 84.4 | — |
| ERQA | 65.4 | 69.4 | — |
This is Gemini's clearest win — and it is not just about benchmark numbers. Gemini 3 was trained end-to-end on text, images, audio, video, and PDFs as a single natively multimodal model. GPT-5.4 was not; its vision capabilities were integrated separately. The difference shows up in practice: Cartwheel's Andrew Carr documented Gemini solving 3D rotation-order bugs that competing models could not handle.
Google deepened this advantage in March 2026 with Gemini Embedding 2 — the first embedding model that maps text, images, video, audio, and PDFs into a single vector space. For teams building retrieval pipelines across mixed-media content, this is a genuine capability gap that no other provider matches.
Deep Think pushes even further: 95 on MMMU-Pro is the highest score in the entire matchup, making it the best model for document-heavy reasoning tasks where both visual understanding and deep thinking are needed.
GPT-5.4 holds its own on OfficeQA-Pro (96 — best in this trio) and ScreenSpot-Pro (85.4). If your multimodal needs are primarily office documents and UI analysis, GPT-5.4 is competitive. If they involve images, video, medical imaging, or cross-format retrieval, Gemini has a design-level advantage that benchmarks understate.
| Benchmark | GPT-5.4 | Gemini 3.1 Pro | Deep Think |
|---|---|---|---|
| MRCRv2 | 97 | 90 | 96 |
| MRCRv2 (64–128K) | 86 | — | — |
| MRCRv2 (128–256K) | 79.3 | — | — |
| LongBench v2 | — | 93 | 94 |
| Context window | 1.05M | 1M | 2M |
Five models now support 1M+ tokens, and independent benchmarks consistently show effective context is roughly 60–70% of the advertised maximum. The real question is not "how big?" but "how well does it degrade?"
GPT-5.4's MRCRv2 curve reveals this clearly: 97 at standard length, 86 at 64–128K, 79.3 at 128–256K. That is a meaningful drop. It still handles long documents better than almost any other model, but the degradation is real.
Gemini 3.1 Pro scores 93 on LongBench v2, which tests practical long-document QA. Deep Think offers a 2M-token context window — the largest in this matchup — and scores 94 on LongBench v2 and 96 on MRCRv2. For legal contract analysis, clinical note processing, or codebase-wide reasoning, Deep Think's combination of context size and recall accuracy is unmatched.
Real production use cases for 1M+ context are now materializing. Legal teams process full contract portfolios. Clinical NLP pipelines ingest longitudinal patient records. Regulatory compliance teams feed entire filing histories. Long context is no longer just marketing — but choosing the right model for your degradation tolerance matters more than the raw number on the spec sheet.
| Benchmark | GPT-5.4 | Gemini 3.1 Pro | Deep Think |
|---|---|---|---|
| tau2Bench | 98.9 | 95.6 | — |
| BrowseComp | 82.7 | 86 | 87 |
| OSWorld | 75 | 68 | 73 |
| GAIA | 48.2 | 46.1 | — |
| WebArena | 62.3 | 58.4 | — |
| tauBench | 78.3 | 76.5 | — |
GPT-5.4 scored 75 on OSWorld, surpassing the 72.4% human baseline — the first mainstream model to do so. It is also the first to unify reasoning, coding, and native computer use in a single model. With 98.9 on tau2Bench and a native screen-control API, GPT-5.4 is the strongest choice for desktop automation and tool-heavy agent workflows.
But the web-agent story is different. Gemini 3.1 Pro leads BrowseComp (86 vs 82.7) and Deep Think leads it further at 87. Google launched the Gemini Interactions API in beta with an explicit agent-focused roadmap, and the Agentic AI Foundation launched under the Linux Foundation in early 2026. The agent ecosystem is consolidating around MCP (97M+ installs by March), Agent-to-Agent (A2A), and Agent User Interaction (AG-UI) protocols.
2026 is definitively the year of agents — 40% of enterprise apps are expected to embed task-specific AI agents by year-end. GPT-5.4 has the agentic edge today, especially for desktop automation (screen-control has no Gemini equivalent). But Google's BrowseComp lead and Interactions API suggest the web-agent gap is closing fast.
| Model | Input (per 1M) | Output (per 1M) | Context | Type |
|---|---|---|---|---|
| GPT-5.4 | $2.50 | $15.00 | 1.05M | Reasoning |
| Gemini 3.1 Pro | $1.25 | $5.00 | 1M | Non-Reasoning |
| Gemini 3.1 Pro (Batch) | $1.00 | $6.00 | 1M | Non-Reasoning |
| Gemini 3.1 Pro (Cached) | $0.20 | $5.00 | 1M | Non-Reasoning |
For a typical workload of 1M input tokens and 200K output tokens:
Gemini is 2.4x cheaper for the same task. At scale, this compounds fast.
| Model | Score | Input | Output |
|---|---|---|---|
| Gemini 3 Flash | 64 | $0.50 | $3.00 |
| GPT-5.4 mini | 62 | $0.75 | $4.50 |
| Gemini 3.1 Flash-Lite | 54 | $0.10 | $0.40 |
| GPT-5.4 nano | 49 | $0.20 | $1.25 |
Google undercuts OpenAI at every tier. Flash-Lite at $0.10/$0.40 is half the cost of GPT-5.4 nano while scoring 5 points higher. This is deliberate: Google is using pricing as a strategic weapon to drive adoption volume, and it is working — Gemini reached 750 million users by March 2026.
The real pressure, though, comes from neither company. DeepSeek V3.2 delivers roughly 90% of GPT-5.4's performance at $0.28 per million input tokens — 9x cheaper than GPT-5.4 and 4.5x cheaper than Gemini 3.1 Pro. The proprietary pricing floor is being set by open-source competitors, not by the duopoly.
GPT-5.4 is a reasoning model. It thinks before it responds — chain-of-thought at inference time with five discrete reasoning levels (none/low/medium/high/xhigh). This adds latency but helps on the hardest problems. For interactive chat, autocomplete, or iterative editing, the delay is noticeable.
Gemini 3.1 Pro is a non-reasoning model. No chain-of-thought overhead means faster time-to-first-token and lower per-response latency. For chatbots, real-time assistants, and high-throughput API pipelines, this matters.
Deep Think is the slowest of the three — it is explicitly designed for "System 2" thinking on problems that lack clear guardrails. Google positions it for research and scientific discovery, not interactive workflows.
The practical trade-off: if your workload is latency-sensitive and does not require competition-level reasoning, Gemini 3.1 Pro's non-reasoning architecture gives it an inherent speed advantage. If you need maximum reasoning depth and can tolerate the wait, GPT-5.4 delivers.
| Use case | Pick | Why |
|---|---|---|
| Competition math / hard science | GPT-5.4 or Deep Think | 99 AIME, 95.2 USAMO, gold-medal IMO |
| Multimodal workflows | Gemini 3.1 Pro | Natively multimodal, not bolted-on |
| Budget-conscious API usage | Gemini 3.1 Pro | Half the cost, 1-point difference |
| Desktop / computer-use agents | GPT-5.4 | 75 OSWorld, native screen-control API |
| Web research agents | Gemini 3.1 Pro | 86 BrowseComp, Interactions API |
| Long-context (>1M tokens) | Deep Think | 2M context, 94 LongBench v2 |
| Enterprise knowledge work | GPT-5.4 | 97 SimpleQA, 96 OfficeQA-Pro |
| Real-world messy codebases | Gemini 3.1 Pro | 72 SWE-Pro vs 57.7 |
| Clean repo-level engineering | GPT-5.4 | 84 SWE-bench Verified, Codex heritage |
| Low-latency interactive use | Gemini 3.1 Pro | Non-reasoning, faster responses |
The gap between OpenAI and Google has never been smaller, and the release cadence shows no signs of slowing down. March 2026 was the most competitive month in AI history, with five frontier models launching within weeks of each other.
OpenAI's roadmap. GPT-5.5 (codenamed "Spud") has reportedly completed pretraining. Altman has signaled major model improvements throughout 2026 without committing to the GPT-6 name — the focus is on shifting from chatbot to agentic OS, with deeper long-term memory and autonomous agent capabilities. Enterprise now exceeds 40% of OpenAI revenue.
Google's roadmap. No official Gemini 4 has been announced, but Google's strategic focus is clear: autonomous AI agents via the Interactions API, deeper multimodal integration, and aggressive pricing to drive the ecosystem. Gemma 4 (open-weight models for reasoning and agentic work) has surpassed 400 million downloads across generations. A next-gen Gemini model is expected late 2026.
The wild cards. DeepSeek V4 (a suspected 1-trillion-parameter model appeared on OpenRouter in March), Claude Mythos (described in internal leaks as a new tier above Opus), and Grok 5 training on xAI's 1-gigawatt Colossus 2 cluster could all reshape the leaderboard before year-end. The open-source gap has effectively collapsed on most benchmarks — GLM-5 sits at 82 overall, Qwen 3.5 at 77 — and the proprietary advantage is increasingly about ecosystem, reliability, and enterprise support rather than raw capability.
Our take. The 1-point gap between GPT-5.4 and Gemini 3.1 Pro is noise. The real divergence is strategic: OpenAI is building depth (reasoning, agents, computer use), Google is building breadth (multimodal, context, pricing). Both are viable foundations for the next wave of AI-native products. The question is not which model is better — it is which bet you want to build on.
GPT-5.4 and Gemini 3.1 Pro are effectively tied. The gap between them (84 vs 83) is smaller than the gap between either model and anything else in the market except a handful of coding-specialized variants. Deep Think adds a third dimension for teams that need PhD-level reasoning with a massive context window.
If you are processing millions of tokens a day and multimodal or real-world coding matters most, Gemini 3.1 Pro is the better value at half the cost. If you need competition-level math, desktop automation, or the deepest knowledge recall, GPT-5.4 earns its premium. And if you are a researcher pushing the frontier on hard science problems, Deep Think is the model nobody is talking about enough.
→ Full leaderboard · Compare GPT-5 vs Gemini · Best for Coding · Best Overall
Is GPT-5 better than Gemini in 2026? GPT-5.4 scores 84 overall on BenchLM to Gemini 3.1 Pro's 83 — effectively a tie. GPT-5.4 leads on math, knowledge, and agentic tasks. Gemini 3.1 Pro leads on multimodal, abstract reasoning, and costs half as much. The right pick depends on your workload.
Which is cheaper, GPT-5 or Gemini? Gemini 3.1 Pro costs $1.25 / $5 per million tokens — half the input cost and a third the output cost of GPT-5.4 ($2.50 / $15). Google's budget tiers are even more aggressive: Flash-Lite at $0.10 / $0.40 undercuts GPT-5.4 nano ($0.20 / $1.25) by half.
Is Gemini better than GPT-5 for coding? It depends on the benchmark. GPT-5.4 leads SWE-bench Verified (84 vs 75) and LiveCodeBench (84 vs 71). Gemini 3.1 Pro leads SWE-Pro (72 vs 57.7) and TerminalBench2 (77 vs 75.1). GPT-5.4 is stronger on clean repo-level tasks; Gemini handles messier real-world software engineering better.
What is Gemini Deep Think and how does it compare to GPT-5? Gemini 3 Pro Deep Think is Google's dedicated reasoning model, designed for science, research, and engineering problems. It scores 99 on AIME 2023, gold-medal on IMO 2025, and 48.4% on Humanity's Last Exam without tools — matching or beating GPT-5.4 on hard reasoning while offering a 2M-token context window.
Should I switch from GPT-5 to Gemini or vice versa? If you're spending heavily on API calls and don't rely on GPT-5.4's math or agentic strengths, switching to Gemini 3.1 Pro can cut costs by 50–70% with comparable performance. If you need competition-level math, desktop automation, or the Codex ecosystem, GPT-5.4 is still the better fit.
All benchmark data is from our leaderboard. Scores are BenchLM overall scores, not raw percentages — see our methodology for details. Compare these models head-to-head on our comparison page.
These rankings update with every new model. We send one email a week with what moved and why.
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