Agentic
Not comparable- GPT Realtime 2
- Not measured
- MiniMax M2.5
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Model comparison
GPT Realtime 2 vs MiniMax M2.5
Updated August 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
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.
Chat turn cost
1K fresh input + 500 output tokens
MiniMax M2.5
MiniMax M2.5 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
Repository review cost
50K fresh input + 3K output tokens
MiniMax M2.5
MiniMax M2.5 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
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
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
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 2 does not fit this workload in one request. MiniMax M2.5 does not fit this workload in one request. MiniMax M2.5 has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
1 MiniMax M2.5 only
- Shared results
- 0
- GPT Realtime 2 only
- 0
- MiniMax M2.5 only
- 1
- 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- GPT Realtime 2
- Not measured
- MiniMax M2.5
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT Realtime 2
- Not measured
- MiniMax M2.5
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- GPT Realtime 2
- Not measured
- MiniMax M2.5
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Math
Not comparable- GPT Realtime 2
- Not measured
- MiniMax M2.5
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT Realtime 2
- Not measured
- MiniMax M2.5
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT Realtime 2
- Not measured
- MiniMax M2.5
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT Realtime 2
- Not measured
- MiniMax M2.5
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | GPT Realtime 2 | MiniMax M2.5 | 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 | Not measured | Not measured | Not comparable0 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 | Not measured | Not measured | Not comparable0 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
- GPT Realtime 2
- $0.016
- Fits in one request
- MiniMax M2.5
- $0.0009
- Fits in one request
MiniMax M2.5 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- GPT Realtime 2
- $0.272
- Fits in one request
- MiniMax M2.5
- $0.0186
- Fits in one request
MiniMax M2.5 has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- GPT Realtime 2
- $0.4
- Does not fit in one request
- MiniMax M2.5
- $0.078
- Does not fit in one request
- Cached input priced at the published list-input rate
GPT Realtime 2 does not fit this workload in one request. MiniMax M2.5 does not fit this workload in one request. MiniMax M2.5 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.
SpecificationGPT Realtime 2MiniMax M2.5
Context window
Maximum documented context; output-token limits may be lower.
GPT Realtime 2
MiniMax M2.5
128K
API model ID
GPT Realtime 2
gpt-realtime-2
OpenAI model documentationMiniMax M2.5
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT Realtime 2
$0.4 per 1M cached input tokens
OpenAI model documentationMiniMax M2.5
Not published
Documented inputs
GPT Realtime 2
Not sourced
MiniMax M2.5
Not sourced
Documented outputs
GPT Realtime 2
Not sourced
MiniMax M2.5
Not sourced
Provider availability
GPT Realtime 2
Not sourced
MiniMax M2.5
Not sourced
Reasoning profile
GPT Realtime 2
Non-Reasoning
MiniMax M2.5
Non-Reasoning
Weight access
GPT Realtime 2
Proprietary
MiniMax M2.5
Proprietary
License
GPT Realtime 2
Proprietary
MiniMax M2.5
Proprietary
Release date
GPT Realtime 2
Not sourced
MiniMax M2.5
2025-10-01
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: $0.272 vs $0.0186. Cache-heavy agent loop: $0.4 vs $0.078.
- Context tradeoff
- Both models list 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
Coding
Vibe Code Bench
GPT Realtime 2—MiniMax M2.514.85%SourceNot directly comparable
Frequently asked questions
Which is better, GPT Realtime 2 or MiniMax M2.5?
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, GPT Realtime 2 or MiniMax M2.5?
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, GPT Realtime 2 or MiniMax M2.5?
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, GPT Realtime 2 or MiniMax M2.5?
For the stated presets, chat costs $0.016 on GPT Realtime 2 and $0.0009 on MiniMax M2.5; repository review costs $0.272 and $0.0186; the cache-heavy agent loop costs $0.4 and $0.078. GPT Realtime 2 does not fit this workload in one request. MiniMax M2.5 does not fit this workload in one request. MiniMax M2.5 has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, GPT Realtime 2 or MiniMax M2.5?
Both models list the same context window, 128K.
Related comparisons
Last updated August 4, 2026
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