Model comparison
Celeris-1 vs Kimi K2.6
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: Celeris-1 unranked (Not scored); Kimi K2.6 #74 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Celeris-1 and Kimi K2.6 share 0 comparable benchmark results. 1 of 8 categories are comparable. 1 result is unique to Celeris-1; 51 to Kimi K2.6.
Updated July 24, 2026- Shared results
- 0
- Celeris-1 only
- 1
- Kimi K2.6 only
- 51
- 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; Kimi K2.6 is the better fit if you want the cheaper token bill or you need the larger 256K context window.
Confidence note. This is a partial-evidence comparison with 0 shared benchmark results across 0 evidence categories; 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 Kimi K2.6 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 $0.95 input / $4.00 output per 1M tokens for Kimi K2.6. Kimi K2.6 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. Kimi K2.6 gives you the larger context window at 256K, compared with 8K for Celeris-1.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Celeris-1 | Kimi K2.6 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Celeris-1$2 input / $6 output | Kimi K2.6$0.95 input / $4 output | Kimi K2.6 has the lower combined listed price. |
| Generation speedtokens per second | Celeris-1Not available | Kimi K2.6Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Celeris-1Not available | Kimi K2.6Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Celeris-18K | Kimi K2.6256K | Kimi K2.6 lists the larger context window. |
Benchmark Deep Dive
Agentic17 benchmarks
| Benchmark | Celeris-1 | Kimi K2.6 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | — | 66.7% | Not comparable |
| BrowseCompSource | — | 83.2% | Not comparable |
| OSWorld-VerifiedSource | — | 73.1% | Not comparable |
| ToolathlonSource | — | 50% | Not comparable |
| MCP AtlasSource | — | 55.9% | Not comparable |
| Claw-EvalSource | — | 62.3% | Not comparable |
| DeepSearchQASource | — | 92.5% | Not comparable |
| WideResearchSource | — | 80.8% | Not comparable |
| AA Agentic IndexSource | — | 30.3% | Not comparable |
| τ²-bench resultsSource | — | 95.9% | Not comparable |
| GDPval-AASource | — | 34.5% | Not comparable |
| GDPval-AASource | — | 1189 | Not comparable |
| APEX-Agents-AASource | — | 28.5% | Not comparable |
| Gert LabsSource | — | 56.82% | Not comparable |
| ResearchClawBenchSource | — | 18.0% | Not comparable |
| OSWorld 2.0Source | — | 4.6% | Not comparable |
| terminalBenchHardSource | — | 43.9% | Not comparable |
Coding10 benchmarks
| Benchmark | Celeris-1 | Kimi K2.6 | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | — | 80.2% | Not comparable |
| LiveCodeBench v6Source | — | 89.6% | Not comparable |
| SWE-bench ProSource | — | 58.6% | Not comparable |
| SWE MultilingualSource | — | 76.7% | Not comparable |
| SciCodeSource | — | 52.2% | Not comparable |
| Terminal-Bench 2.0Source | — | 66.7% | Not comparable |
| Vibe Code BenchSource | — | 37.89% | Not comparable |
| cursorBench31Source | — | 47.6% | Not comparable |
| AA Coding IndexSource | — | 61.8% | Not comparable |
| AA-SciCodeSource | — | 53.5% | Not comparable |
Reasoning2 benchmarks
KnowledgeCeleris-1 wins10 benchmarks
| Benchmark | Celeris-1 | Kimi K2.6 | Result |
|---|---|---|---|
| MMLU-ProSource | 75.9% | — | Not comparable |
| GPQASource | — | 90.5% | Not comparable |
| GPQA-DSource | — | 90.5% | Not comparable |
| HLESource | — | 34.7% | Not comparable |
| Artificial Analysis Intelligence IndexSource | — | 44.2% | Not comparable |
| AA-GPQA DiamondSource | — | 91.1% | Not comparable |
| AA-HLESource | — | 35.9% | Not comparable |
| AA-Omniscience IndexSource | — | 6.4% | Not comparable |
| AA-Omniscience AccuracySource | — | 32.8% | Not comparable |
| AA-Omniscience Hallucination RateSource | — | 39.3% | Not comparable |
Math5 benchmarks
Multimodal7 benchmarks
Inst. Following1 benchmarks
| Benchmark | Celeris-1 | Kimi K2.6 | Result |
|---|---|---|---|
| AA-IFBenchSource | — | 76.0% | Not comparable |
Frequently Asked Questions (2)
Which is better, Celeris-1 or Kimi K2.6?
Celeris-1 and Kimi K2.6 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 Kimi K2.6?
Celeris-1 has the edge for knowledge tasks in this comparison, averaging 75.9 versus 42.2. Kimi K2.6 stays close enough that the answer can still flip depending on your workload.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
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