Agentic
Not comparable- Celeris-1
- Not measured
- Quasar 438B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Celeris-1 vs Quasar 438B
Updated September 2, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.
0 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.
Which one for your work
Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.
Long documents
Prompts that approach the documented context limit
Quasar 438B
Quasar 438B has the larger documented context window.
Confidence: documented
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
Confidence: limited
Chat turn cost
1K fresh input + 500 output tokens
Not enough matched evidence
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Celeris-1 does not fit this workload in one request.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Celeris-1 does not fit this workload in one request. Celeris-1 has no published cached-input rate, so cached tokens use its listed input rate. Quasar 438B has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
Repository review cost
50K fresh input + 3K output tokens
Not enough matched evidence
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Celeris-1 does not fit this workload in one request.
Confidence: listed-rates
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
4 Celeris-1 only
2 Quasar 438B only
- Shared results
- 0
- Celeris-1 only
- 4
- Quasar 438B only
- 2
- Like-for-like categories
- 0 / 8
Category results, on a stated basis
Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.
Coding
Not comparable- Celeris-1
- Not measured
- Quasar 438B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Reasoning
Not comparable- Celeris-1
- Not measured
- Quasar 438B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- Celeris-1
- 75.9
- Quasar 438B
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Math
Not comparable- Celeris-1
- Not measured
- Quasar 438B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- Celeris-1
- Not measured
- Quasar 438B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- Celeris-1
- Not measured
- Quasar 438B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- Celeris-1
- 80.8
- Quasar 438B
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
| Category | Celeris-1 | Quasar 438B | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Coding | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Knowledge | 75.9 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Math | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Instruction following | 80.8 | Not measured | Not comparable1 vs 0 rows | Not comparable |
Shape of the matched evidence
Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
What each workload costs
Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.
Chat turn
1K fresh input + 500 output tokens
- Celeris-1
- $0.00055
- Does not fit in one request
- Quasar 438B
- $0.0015
- Fits in one request
Celeris-1 does not fit this workload in one request.
Repository review
50K fresh input + 3K output tokens
- Celeris-1
- $0.0121
- Does not fit in one request
- Quasar 438B
- $0.0354
- Fits in one request
Celeris-1 does not fit this workload in one request.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Celeris-1
- $0.051
- Does not fit in one request
- Cached input priced at the published list-input rate
- Quasar 438B
- $0.15
- Fits in one request
- Cached input priced at the published list-input rate
Celeris-1 does not fit this workload in one request. Celeris-1 has no published cached-input rate, so cached tokens use its listed input rate. Quasar 438B has no published cached-input rate, so cached tokens use its listed input rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationCeleris-1Quasar 438B
Context window
Maximum documented context; output-token limits may be lower.
Celeris-1
131,072 tokens
Celeris-1 model guideQuasar 438B
API model ID
Celeris-1
celeris-1
Celeris-1 model guideQuasar 438B
quasar-438b
CompactifAI Quasar 438B documentationCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Celeris-1
Not published
Celeris API pricingQuasar 438B
Not published
CompactifAI API pricingDocumented inputs
Celeris-1
text, image
Celeris-1 model guideQuasar 438B
Not sourced
Documented outputs
Celeris-1
Quasar 438B
Not sourced
Provider availability
Celeris-1
Generally Available · Celeris OpenAI-compatible API (United States)
Celeris availabilityQuasar 438B
Generally Available · CompactifAI API
CompactifAI Quasar 438B documentationReasoning profile
Celeris-1
Non-Reasoning
Quasar 438B
Reasoning
Weight access
Celeris-1
Proprietary
Quasar 438B
Proprietary
License
Celeris-1
Proprietary
Quasar 438B
Proprietary
Release date
Celeris-1
2026-07-22
Quasar 438B
2026-09-02
If you already use one of these models
- Deployment change
- The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
- Quality signal
- The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
- Workload cost
- Repository review: $0.0121 vs $0.0354. Cache-heavy agent loop: $0.051 vs $0.15.
- Context tradeoff
- Quasar 438B has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence6 rows
Agentic
Terminal-Bench 2.1
Celeris-1—Quasar 438B69.3%SourceNot directly comparable
Coding
Terminal-Bench 2.1
Celeris-1—Quasar 438B69.3%SourceNot directly comparable
Reasoning
DROP
Celeris-181.4%SourceQuasar 438B—Not directly comparable
Knowledge
MMLU-Pro
Celeris-175.9%SourceQuasar 438B—Not directly comparable
Math
GSM8K
Celeris-193.7%SourceQuasar 438B—Not directly comparable
Instruction following
IFEval
Celeris-180.8%SourceQuasar 438B—Not directly comparable
Frequently asked questions
Which is better, Celeris-1 or Quasar 438B?
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, Celeris-1 or Quasar 438B?
The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.
Which is better for agentic tasks, Celeris-1 or Quasar 438B?
The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.
Which costs less, Celeris-1 or Quasar 438B?
For the stated presets, chat costs $0.00055 on Celeris-1 and $0.0015 on Quasar 438B; repository review costs $0.0121 and $0.0354; the cache-heavy agent loop costs $0.051 and $0.15. Celeris-1 does not fit this workload in one request. Celeris-1 has no published cached-input rate, so cached tokens use its listed input rate. Quasar 438B has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, Celeris-1 or Quasar 438B?
Quasar 438B has the larger documented context window: 1M, compared with 131,072 tokens.
Related comparisons
Last updated September 2, 2026
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