Model comparison
Celeris-1 vs ZAYA1-8B
Head-to-head evidence from 1 shared benchmark result across 1 category. Overall scores shown here use the public BenchAlign v5 ranking lane.
Evidence parity. Celeris-1 and ZAYA1-8B share 1 comparable benchmark result. 1 of 8 categories are comparable. 0 results are unique to Celeris-1; 10 to ZAYA1-8B.
Updated July 24, 2026- Shared results
- 1
- Celeris-1 only
- 0
- ZAYA1-8B only
- 10
- Comparable categories
- 1 / 8
Treat this as a split decision. Celeris-1 makes more sense if knowledge is the priority or you would rather avoid the extra latency and token burn of a reasoning model; ZAYA1-8B is the better fit if you want the cheaper token bill or you need the larger 131K context window.
Confidence note. This is a partial-evidence comparison with 1 shared benchmark result across 1 evidence category; 1 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
Celeris-1 and ZAYA1-8B finish on the same BenchAlign overall score, so this is less about a single winner and more about where the edge shows up. The BenchAlign headline says tie; the benchmark table is where the real choice happens.
Celeris-1 is also the more expensive model on tokens at $2.00 input / $6.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for ZAYA1-8B. That is roughly Infinityx on output cost alone. ZAYA1-8B is the reasoning model in the pair, while Celeris-1 is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. ZAYA1-8B gives you the larger context window at 131K, compared with 8K for Celeris-1.
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 | Celeris-1 | Δ | ZAYA1-8B |
|---|---|---|---|
| Knowledge | Celeris-175.9 | Margin← 2.3 | ZAYA1-8B73.6 |
| Math | Celeris-1Not measured | MarginNo overlap | ZAYA1-8B80.4 |
| Inst. Following | Celeris-1Not measured | MarginNo overlap | ZAYA1-8B64.1 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
MMLU-Pro
KnowledgeA 75.9%B 74.2%Winner: Celeris-1Δ 1.7MMLU-Pro: Celeris-1 scored 75.9%; ZAYA1-8B scored 74.2%. Celeris-1 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Celeris-1 | ZAYA1-8B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Celeris-1$2 input / $6 output | ZAYA1-8B$0 input / $0 output | ZAYA1-8B has the lower combined listed price. |
| Generation speedtokens per second | Celeris-1Not available | ZAYA1-8BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Celeris-1Not available | ZAYA1-8BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Celeris-18K | ZAYA1-8B131K | ZAYA1-8B lists the larger context window. |
Benchmark Deep Dive
Frequently Asked Questions (2)
Which is better, Celeris-1 or ZAYA1-8B?
Celeris-1 and ZAYA1-8B are tied on the BenchAlign overall score, so the right pick depends on which category matters most for your use case.
Which is better for knowledge tasks, Celeris-1 or ZAYA1-8B?
Celeris-1 has the edge for knowledge tasks in this comparison, averaging 75.9 versus 73.6. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
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