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Model comparison

Celeris-1 vs GPT Realtime 1.5

Updated August 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Celeris-1

Celeris

—

Evidence status unavailable

90% interval unavailable

GPT Realtime 1.5

OpenAI

—

Evidence status unavailable

90% interval unavailable

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

    Celeris-1

    Celeris-1 has the larger documented context window.

    Confidence: documented

  • Chat turn cost

    1K fresh input + 500 output tokens

    Celeris-1

    Celeris-1 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

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

  • 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. GPT Realtime 1.5 does not fit this workload in one request. Celeris-1 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. GPT Realtime 1.5 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.

Shared results
0
Celeris-1 only
1
GPT Realtime 1.5 only
0
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.

Agentic

Not comparable
Celeris-1
Not measured
GPT Realtime 1.5
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Coding

Not comparable
Celeris-1
Not measured
GPT Realtime 1.5
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
Celeris-1
Not measured
GPT Realtime 1.5
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
Celeris-1
75.9
GPT Realtime 1.5
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Math

Not comparable
Celeris-1
Not measured
GPT Realtime 1.5
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Celeris-1
Not measured
GPT Realtime 1.5
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Celeris-1
Not measured
GPT Realtime 1.5
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Celeris-1
Not measured
GPT Realtime 1.5
Not measured
Weighted basis
0 vs 0 rows
Reading
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
$0.005
Fits in one request
GPT Realtime 1.5
$0.012
Fits in one request

Celeris-1 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Celeris-1
$0.118
Fits in one request
GPT Realtime 1.5
$0.248
Does not fit in one request

GPT Realtime 1.5 does not fit this workload in one request.

Cache-heavy agent loop

200K cached + 20K fresh input + 10K output tokens

Celeris-1
$0.5
Does not fit in one request
Cached input priced at the published list-input rate
GPT Realtime 1.5
$0.32
Does not fit in one request

Celeris-1 does not fit this workload in one request. GPT Realtime 1.5 does not fit this workload in one request. Celeris-1 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.

Context window

Maximum documented context; output-token limits may be lower.

Celeris-1

128K

GPT Realtime 1.5

32K

OpenAI model documentation

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

Celeris-1

Not published

GPT Realtime 1.5

$0.4 per 1M cached input tokens

OpenAI model documentation

Documented inputs

Celeris-1

Not sourced

GPT Realtime 1.5

Not sourced

Documented outputs

Celeris-1

Not sourced

GPT Realtime 1.5

Not sourced

Provider availability

Celeris-1

Not sourced

GPT Realtime 1.5

Not sourced

Reasoning profile

Celeris-1

Non-Reasoning

GPT Realtime 1.5

Non-Reasoning

Weight access

Celeris-1

Proprietary

GPT Realtime 1.5

Proprietary

License

Celeris-1

Proprietary

GPT Realtime 1.5

Proprietary

Release date

Celeris-1

2026-07-22

GPT Realtime 1.5

2026-02-01

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: $0.118 vs $0.248. Cache-heavy agent loop: $0.5 vs $0.32.
Context tradeoff
Celeris-1 has the larger documented window (128K).

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 evidence1 rows

Knowledge

  • MMLU-Pro

    Celeris-175.9%
    Source
    GPT Realtime 1.5—

    Not directly comparable

Frequently asked questions

Which is better, Celeris-1 or GPT Realtime 1.5?

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 GPT Realtime 1.5?

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 GPT Realtime 1.5?

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 GPT Realtime 1.5?

For the stated presets, chat costs $0.005 on Celeris-1 and $0.012 on GPT Realtime 1.5; repository review costs $0.118 and $0.248; the cache-heavy agent loop costs $0.5 and $0.32. Celeris-1 does not fit this workload in one request. GPT Realtime 1.5 does not fit this workload in one request. Celeris-1 has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, Celeris-1 or GPT Realtime 1.5?

Celeris-1 has the larger documented context window: 128K, compared with 32K.

Related comparisons

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Last updated August 4, 2026

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