Model comparison
Gemini 2.5 Flash vs GPT-4 Turbo
Head-to-head evidence from 3 shared benchmark results across 2 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Gemini 2.5 Flash #130 (Supported); GPT-4 Turbo #188 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Gemini 2.5 Flash and GPT-4 Turbo share 3 comparable benchmark results. 0 of 8 categories are comparable. 12 results are unique to Gemini 2.5 Flash; 1 to GPT-4 Turbo.
Updated July 20, 2026- Shared results
- 3
- Gemini 2.5 Flash only
- 12
- GPT-4 Turbo only
- 1
- Comparable categories
- 0 / 8
Benchmark data for Gemini 2.5 Flash and GPT-4 Turbo is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 3 shared benchmark results across 2 evidence categories; 0 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
BenchLM has partial data for these models, but not enough overlapping benchmark coverage to produce a fair score-level comparison yet.
GPT-4 Turbo is priced at $10.00 input / $30.00 output per 1M tokens, versus $0.30 input / $2.50 output per 1M tokens for Gemini 2.5 Flash. Gemini 2.5 Flash has the larger context window at 1M, compared with 128K for GPT-4 Turbo.
Category breakdown
Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.
| Category | Gemini 2.5 Flash | Δ | GPT-4 Turbo |
|---|---|---|---|
| Math | Gemini 2.5 Flash4.7 | MarginNo overlap | GPT-4 TurboNot measured |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Gemini 2.5 Flash | GPT-4 Turbo | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Gemini 2.5 Flash$0.3 input / $2.5 output | GPT-4 Turbo$10 input / $30 output | Gemini 2.5 Flash has the lower combined listed price. |
| Generation speedtokens per second | Gemini 2.5 Flash221 tok/s | GPT-4 Turbo30 tok/s | Gemini 2.5 Flash has the higher measured throughput. |
| First-answer latencyseconds to first token | Gemini 2.5 Flash0.50 s | GPT-4 Turbo2.84 s | Gemini 2.5 Flash reaches the first token sooner. |
| Context windowmaximum listed tokens | Gemini 2.5 Flash1M | GPT-4 Turbo128K | Gemini 2.5 Flash lists the larger context window. |
Benchmark Deep Dive
Agentic1 benchmarks
| Benchmark | Gemini 2.5 Flash | GPT-4 Turbo | Result |
|---|---|---|---|
| τ²-bench resultsSource | 14.9% | — | Not comparable |
Coding2 benchmarks
Reasoning2 benchmarks
Knowledge6 benchmarks
| Benchmark | Gemini 2.5 Flash | GPT-4 Turbo | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 14.1% | 7.9% | Gemini 2.5 Flash leads |
| AA-GPQA DiamondSource | 68.3% | — | Not comparable |
| AA-HLESource | 5.1% | 3.3% | Gemini 2.5 Flash leads |
| AA-Omniscience IndexSource | -42.0% | — | Not comparable |
| AA-Omniscience AccuracySource | 26.5% | — | Not comparable |
| AA-Omniscience Hallucination RateSource | 93.3% | — | Not comparable |
Math2 benchmarks
Multimodal2 benchmarks
Inst. Following1 benchmarks
| Benchmark | Gemini 2.5 Flash | GPT-4 Turbo | Result |
|---|---|---|---|
| AA-IFBenchSource | 39.0% | — | Not comparable |
Frequently Asked Questions (3)
Can I compare Gemini 2.5 Flash and GPT-4 Turbo on BenchLM yet?
Not fully yet. BenchLM is tracking both models, but the sourced benchmark breakdown for this comparison is still coming soon.
Why does this comparison show “coming soon”?
BenchLM only shows category winners and benchmark-level calls when we have sourced results that can be compared fairly. For these models, the public benchmark coverage is not complete enough yet.
What data is available for Gemini 2.5 Flash and GPT-4 Turbo today?
Gemini 2.5 Flash: $0.30 input / $2.50 output per 1M tokens GPT-4 Turbo: $10.00 input / $30.00 output per 1M tokens Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.
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