Model comparison
Claude Haiku 4.5 vs GPT-5 (high)
Head-to-head evidence from 2 shared benchmark results across 2 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Claude Haiku 4.5 #77 (Estimated); GPT-5 (high) #63 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Claude Haiku 4.5 and GPT-5 (high) share 2 comparable benchmark results. 0 of 8 categories are comparable. 3 results are unique to Claude Haiku 4.5; 18 to GPT-5 (high).
Updated July 20, 2026- Shared results
- 2
- Claude Haiku 4.5 only
- 3
- GPT-5 (high) only
- 18
- Comparable categories
- 0 / 8
Benchmark data for Claude Haiku 4.5 and GPT-5 (high) is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 2 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-5 (high) is priced at $1.25 input / $10.00 output per 1M tokens, versus $1.00 input / $5.00 output per 1M tokens for Claude Haiku 4.5. Claude Haiku 4.5 has the larger context window at 200K, compared with 128K for GPT-5 (high).
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 | Claude Haiku 4.5 | Δ | GPT-5 (high) |
|---|---|---|---|
| Coding | Claude Haiku 4.573.3 | MarginNo overlap | GPT-5 (high)Not measured |
| Math | Claude Haiku 4.54.9 | MarginNo overlap | GPT-5 (high)Not measured |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Claude Haiku 4.5 | GPT-5 (high) | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Claude Haiku 4.5$1 input / $5 output | GPT-5 (high)$1.25 input / $10 output | Claude Haiku 4.5 has the lower combined listed price. |
| Generation speedtokens per second | Claude Haiku 4.5Not available | GPT-5 (high)83 tok/s | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Claude Haiku 4.5Not available | GPT-5 (high)36.28 s | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Claude Haiku 4.5200K | GPT-5 (high)128K | Claude Haiku 4.5 lists the larger context window. |
Benchmark Deep Dive
Agentic5 benchmarks
Coding4 benchmarks
Reasoning2 benchmarks
Knowledge6 benchmarks
| Benchmark | Claude Haiku 4.5 | GPT-5 (high) | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | — | 34.7% | Not comparable |
| AA-GPQA DiamondSource | — | 85.4% | Not comparable |
| AA-HLESource | — | 26.5% | Not comparable |
| AA-Omniscience IndexSource | — | -8.1% | Not comparable |
| AA-Omniscience AccuracySource | — | 40.7% | Not comparable |
| AA-Omniscience Hallucination RateSource | — | 82.1% | Not comparable |
Math3 benchmarks
Multimodal2 benchmarks
Inst. Following1 benchmarks
| Benchmark | Claude Haiku 4.5 | GPT-5 (high) | Result |
|---|---|---|---|
| AA-IFBenchSource | — | 73.1% | Not comparable |
Frequently Asked Questions (3)
Can I compare Claude Haiku 4.5 and GPT-5 (high) 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 Claude Haiku 4.5 and GPT-5 (high) today?
Claude Haiku 4.5: $1.00 input / $5.00 output per 1M tokens GPT-5 (high): $1.25 input / $10.00 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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