Model comparison
Celeris-1 vs Qwen3.5-27B
Head-to-head evidence from 1 shared benchmark result across 1 category. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Celeris-1 unranked (Not scored); Qwen3.5-27B #45 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Celeris-1 and Qwen3.5-27B share 1 comparable benchmark result. 1 of 8 categories are comparable. 0 results are unique to Celeris-1; 27 to Qwen3.5-27B.
Updated July 24, 2026- Shared results
- 1
- Celeris-1 only
- 0
- Qwen3.5-27B only
- 27
- Comparable categories
- 1 / 8
Treat this as a split decision. Celeris-1 makes more sense if you would rather avoid the extra latency and token burn of a reasoning model; Qwen3.5-27B is the better fit if knowledge is the priority or you want the cheaper token bill.
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 Qwen3.5-27B 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 Qwen3.5-27B. That is roughly Infinityx on output cost alone. Qwen3.5-27B 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. Qwen3.5-27B gives you the larger context window at 262K, 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 | Qwen3.5-27B |
|---|---|---|---|
| Knowledge | Celeris-175.9 | Margin→ 6.8 | Qwen3.5-27B82.7 |
| Agentic | Celeris-1Not measured | MarginNo overlap | Qwen3.5-27B52.0 |
| Coding | Celeris-1Not measured | MarginNo overlap | Qwen3.5-27B64.9 |
| Reasoning | Celeris-1Not measured | MarginNo overlap | Qwen3.5-27B60.6 |
| Multilingual | Celeris-1Not measured | MarginNo overlap | Qwen3.5-27B82.2 |
| Inst. Following | Celeris-1Not measured | MarginNo overlap | Qwen3.5-27B95.0 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
MMLU-Pro
KnowledgeA 75.9%B 86.1%Winner: Qwen3.5-27BΔ 10.2MMLU-Pro: Celeris-1 scored 75.9%; Qwen3.5-27B scored 86.1%. Qwen3.5-27B wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Celeris-1 | Qwen3.5-27B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Celeris-1$2 input / $6 output | Qwen3.5-27B$0 input / $0 output | Qwen3.5-27B has the lower combined listed price. |
| Generation speedtokens per second | Celeris-1Not available | Qwen3.5-27BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Celeris-1Not available | Qwen3.5-27BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Celeris-18K | Qwen3.5-27B262K | Qwen3.5-27B lists the larger context window. |
Benchmark Deep Dive
Agentic5 benchmarks
Coding3 benchmarks
Reasoning3 benchmarks
KnowledgeQwen3.5-27B wins9 benchmarks
| Benchmark | Celeris-1 | Qwen3.5-27B | Result |
|---|---|---|---|
| MMLU-ProSource | 75.9% | 86.1% | Qwen3.5-27B leads |
| SuperGPQASource | — | 65.6% | Not comparable |
| GPQASource | — | 85.5% | Not comparable |
| Artificial Analysis Intelligence IndexSource | — | 33.8% | Not comparable |
| AA-GPQA DiamondSource | — | 85.8% | Not comparable |
| AA-HLESource | — | 22.2% | Not comparable |
| AA-Omniscience IndexSource | — | -42.0% | Not comparable |
| AA-Omniscience AccuracySource | — | 21.0% | Not comparable |
| AA-Omniscience Hallucination RateSource | — | 79.7% | Not comparable |
Multilingual1 benchmarks
| Benchmark | Celeris-1 | Qwen3.5-27B | Result |
|---|---|---|---|
| MMLU-ProXSource | — | 82.2% | Not comparable |
Multimodal5 benchmarks
Frequently Asked Questions (2)
Which is better, Celeris-1 or Qwen3.5-27B?
Celeris-1 and Qwen3.5-27B 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 Qwen3.5-27B?
Qwen3.5-27B has the edge for knowledge tasks in this comparison, averaging 82.7 versus 75.9. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
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