Model comparison
Gemini 2.5 Flash vs GPT-5.2 Instant
Head-to-head evidence from 0 shared benchmark results across 0 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Gemini 2.5 Flash #134 (Supported); GPT-5.2 Instant #65 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Gemini 2.5 Flash and GPT-5.2 Instant share 0 comparable benchmark results. 0 of 8 categories are comparable. 15 results are unique to Gemini 2.5 Flash; 0 to GPT-5.2 Instant.
Updated July 28, 2026- Shared results
- 0
- Gemini 2.5 Flash only
- 15
- GPT-5.2 Instant only
- 0
- Comparable categories
- 0 / 8
Benchmark data for Gemini 2.5 Flash and GPT-5.2 Instant is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 0 shared benchmark results across 0 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 does not have sourced benchmark coverage for GPT-5.2 Instant yet. This comparison is currently limited to metadata such as context window, reasoning mode, and pricing where available.
GPT-5.2 Instant is priced at $1.50 input / $6.00 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-5.2 Instant.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Gemini 2.5 Flash | GPT-5.2 Instant | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Gemini 2.5 Flash$0.3 input / $2.5 output | GPT-5.2 Instant$1.5 input / $6 output | Gemini 2.5 Flash has the lower combined listed price. |
| Generation speedtokens per second | Gemini 2.5 Flash221 tok/s | GPT-5.2 InstantNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Gemini 2.5 Flash0.50 s | GPT-5.2 InstantNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Gemini 2.5 Flash1M | GPT-5.2 Instant128K | Gemini 2.5 Flash lists the larger context window. |
Benchmark Deep Dive
Agentic1 benchmarks
| Benchmark | Gemini 2.5 Flash | GPT-5.2 Instant | Result |
|---|---|---|---|
| τ²-bench resultsSource | 14.9% | — | Not comparable |
Coding1 benchmarks
| Benchmark | Gemini 2.5 Flash | GPT-5.2 Instant | Result |
|---|---|---|---|
| AA-SciCodeSource | 29.1% | — | Not comparable |
Reasoning2 benchmarks
Knowledge6 benchmarks
| Benchmark | Gemini 2.5 Flash | GPT-5.2 Instant | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 14.1% | — | Not comparable |
| AA-GPQA DiamondSource | 68.3% | — | Not comparable |
| AA-HLESource | 5.1% | — | Not comparable |
| 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-5.2 Instant | Result |
|---|---|---|---|
| AA-IFBenchSource | 39.0% | — | Not comparable |
Frequently Asked Questions (3)
Can I compare Gemini 2.5 Flash and GPT-5.2 Instant 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-5.2 Instant today?
Gemini 2.5 Flash: $0.30 input / $2.50 output per 1M tokens GPT-5.2 Instant: $1.50 input / $6.00 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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