Skip to main content

Model comparison

Cosmos3-Edge vs GPT Realtime mini

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

Cosmos3-Edge

NVIDIA

Evidence status unavailable

90% interval unavailable

GPT Realtime mini

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

    Cosmos3-Edge

    Cosmos3-Edge 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

    A complete comparable API-rate estimate is not available for both models.

    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. GPT Realtime mini does not fit this workload in one request. Cosmos3-Edge has no comparable published API token rate.

    Confidence: listed-rates

  • 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 mini does not fit this workload in one request. Cosmos3-Edge has no comparable published API token rate.

    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.

Evidence parity totals are not available.
Shared results
0
Cosmos3-Edge only
0
GPT Realtime mini 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
Cosmos3-Edge
Not measured
GPT Realtime mini
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Coding

Not comparable
Cosmos3-Edge
Not measured
GPT Realtime mini
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
Cosmos3-Edge
Not measured
GPT Realtime mini
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
Cosmos3-Edge
Not measured
GPT Realtime mini
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Math

Not comparable
Cosmos3-Edge
Not measured
GPT Realtime mini
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Cosmos3-Edge
Not measured
GPT Realtime mini
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Cosmos3-Edge
Not measured
GPT Realtime mini
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Cosmos3-Edge
Not measured
GPT Realtime mini
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

Cosmos3-Edge
Self-hosted; infrastructure cost varies
Fits in one request
GPT Realtime mini
$0.0018
Fits in one request

Cosmos3-Edge has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Cosmos3-Edge
Self-hosted; infrastructure cost varies
Fits in one request
GPT Realtime mini
$0.0372
Does not fit in one request

GPT Realtime mini does not fit this workload in one request. Cosmos3-Edge has no comparable published API token rate.

Cache-heavy agent loop

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

Cosmos3-Edge
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable
GPT Realtime mini
$0.048
Does not fit in one request

GPT Realtime mini does not fit this workload in one request. Cosmos3-Edge has no comparable published API token 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.

Cosmos3-Edge

256K

GPT Realtime mini

Cached-input rate

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

Cosmos3-Edge

No comparable hosted API rate

GPT Realtime mini

$0.06 per 1M cached input tokens

OpenAI model documentation

Documented inputs

Cosmos3-Edge

Not sourced

GPT Realtime mini

Not sourced

Documented outputs

Cosmos3-Edge

Not sourced

GPT Realtime mini

Not sourced

Provider availability

Cosmos3-Edge

Not sourced

GPT Realtime mini

Not sourced

Reasoning profile

Cosmos3-Edge

Reasoning

GPT Realtime mini

Non-Reasoning

Weight access

Cosmos3-Edge

Open Weight

GPT Realtime mini

Proprietary

License

Cosmos3-Edge

Open Weight

GPT Realtime mini

Proprietary

Release date

Cosmos3-Edge

2026-07-20

GPT Realtime mini

Not sourced

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
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Cosmos3-Edge has the larger documented window (256K).

Run the same representative tasks against both endpoints before changing production traffic.

Frequently asked questions

Which is better, Cosmos3-Edge or GPT Realtime mini?

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, Cosmos3-Edge or GPT Realtime mini?

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, Cosmos3-Edge or GPT Realtime mini?

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, Cosmos3-Edge or GPT Realtime mini?

Both models do not have comparable published API token rates, so this page does not name a universal price winner.

Which has the larger context window, Cosmos3-Edge or GPT Realtime mini?

Cosmos3-Edge has the larger documented context window: 256K, compared with 32K.

Related comparisons

Last updated August 4, 2026

Watch Cosmos3-Edge vs GPT Realtime mini

One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.

Read a sample issue

Join 2,000+ readers.