Head-to-head comparison
GPT-5.4 vs. Gemini 3.7 Flash
Compare published benchmark results from matching versions and cohorts, API pricing, and technical specifications. Any documented test setup differences remain visible.
2
shared result series
2
distinct benchmarks
2026-08-16
latest retrieval
Change model selection
Specifications and cost
General specifications and standard API pricing
Undisclosed parameter counts and knowledge cutoffs stay marked as unavailable. The table shows direct standard token pricing, including documented context tiers. Batch, fast, priority, regional surcharges, tool calls, and cloud platform pricing are excluded.
| Attribute | GPT-5.4 | Gemini 3.7 Flash |
|---|---|---|
| Provider | OpenAI | |
| Released | Mar 2026 | Aug 13, 2026 |
| Availability | Active | Active |
| Model type | Proprietary | Proprietary |
| Parameters | Not published | Not published |
| Architecture | Not published | Not published |
| Context window | 1,050,000 tokens | 1,048,576 tokens |
| Knowledge cutoff | August 31, 2025 | March 2026 |
| Technical sources | Model | Model, Knowledge cutoff |
| API pricing per 1M tokens | ||
| API input | $2.5 ≤272K / $5 >272K | $0.75 |
| API output | $15 ≤272K / $22.5 >272K | $3.75 |
| Cache read | $0.25 ≤272K / $0.5 >272K | $0.07 |
| Cache write (5 min.) | Not listed | Not listed |
| Cache write (1 hr.) | Not listed | Not listed |
| Price verified | 07/30/2026Source | 08/16/2026Source |
Performance
Shared benchmarks
Only published results with the same benchmark version, task, metric, and comparison cohort are paired. Different reasoning levels, agents, harnesses, or output limits appear directly in the table.
| Benchmark and task | GPT-5.4 | Gemini 3.7 Flash | Comparability | Source |
|---|---|---|---|---|
| Harvey's Legal Agent BenchmarkOverall · Task fully resolved | 0 % | 8.75 %Winner | 1task resolution rateHigher is betterCohort: vals-legal-agent-benchmark:1:overallNo documented setup differenceWinner: Gemini 3.7 FlashGPT-5.4: Snapshot: gpt-5.4-2026-03-05 Gemini 3.7 Flash: No further settings disclosed | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-14Import ID: vals-hlab-2026-08-16-bae90566df2fRetrieved 2026-08-16 |
| Vibe Code Bench v1.1Overall | 67.421 % | 70.395 %Winner | 1.1accuracyHigher is betterCohort: vals-vibe-code-bench:1.1:overallNo documented setup differenceWinner: Gemini 3.7 FlashGPT-5.4: Snapshot: gpt-5.4-2026-03-05, Harness: OpenHands Gemini 3.7 Flash: Harness: OpenHands | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-13Import ID: vals-vibe-code-2026-08-16-a73c9770bcc4Retrieved 2026-08-16 |
How to read this comparison
Winning one benchmark is not an overall verdict
Your workload, budget, and required context length matter most. A coding benchmark says little about visual understanding or agent performance.
Prices use official API rates per 1M tokens. Web subscriptions and cloud platform surcharges can differ.
Every result links to its measurement source and retrieval date. Labels distinguish vendor reports, official benchmark leaderboards, and independent evaluations. Disputed or archived results are excluded.
More matchups
Related LLM comparisons
Compare GPT-5.4 and Gemini 3.7 Flash with other leading models using the same data and benchmark logic.
View the complete LLM comparison