Agentic
Not comparable- GPT Realtime
- Not ranked
- Solar Pro 4
- Not ranked
- Basis
- BenchAlign lane · 0 vs 4 public rows
- Reading
- Not comparable
GPT Realtime vs Solar Pro 4
Updated September 10, 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 resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Share or export
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
Solar Pro 4
Solar Pro 4 has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
Solar Pro 4
Solar Pro 4 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
GPT Realtime and Solar Pro 4 are not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GPT Realtime and Solar Pro 4 are not ranked on the public lane for agentic, so no winner is named for agentic.
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. GPT Realtime does not fit this workload in one request.
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 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.
10 Solar Pro 4 only
- Shared results
- 0
- GPT Realtime only
- 0
- Solar Pro 4 only
- 10
- Like-for-like categories
- 0 / 8
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.
Coding
Not comparable- GPT Realtime
- Not ranked
- Solar Pro 4
- Not ranked
- Basis
- BenchAlign lane · 0 vs 3 public rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT Realtime
- Not ranked
- Solar Pro 4
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- GPT Realtime
- Not ranked
- Solar Pro 4
- Not ranked
- Basis
- BenchAlign lane · 0 vs 2 public rows
- Reading
- Not comparable
Math
Not comparable- GPT Realtime
- Not ranked
- Solar Pro 4
- Not ranked
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT Realtime
- Not ranked
- Solar Pro 4
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT Realtime
- Not ranked
- Solar Pro 4
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT Realtime
- Not ranked
- Solar Pro 4
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | GPT Realtime | Solar Pro 4 | Basis | Reading |
|---|---|---|---|---|
| Agentic | Not ranked | Not ranked | Not comparableBenchAlign lane · 0 vs 4 public rows | Not comparable |
| Coding | Not ranked | Not ranked | Not comparableBenchAlign lane · 0 vs 3 public rows | Not comparable |
| Reasoning | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | Not ranked | Not ranked | Not comparableBenchAlign lane · 0 vs 2 public rows | Not comparable |
| Math | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
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
- $0.012
- Fits in one request
- Solar Pro 4
- $0.0009
- Fits in one request
Solar Pro 4 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- GPT Realtime
- $0.248
- Does not fit in one request
- Solar Pro 4
- $0.0186
- Fits in one request
GPT Realtime does not fit this workload in one request.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- GPT Realtime
- $0.32
- Does not fit in one request
- Solar Pro 4
- $0.03
- Fits in one request
GPT Realtime does not fit this workload in one request.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGPT RealtimeSolar Pro 4
Context window
Maximum documented context; output-token limits may be lower.
GPT Realtime
Solar Pro 4
API model ID
GPT Realtime
gpt-realtime
OpenAI model documentationSolar Pro 4
solar-pro4
Upstage Solar Pro 4 launch postCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT Realtime
$0.4 per 1M cached input tokens
OpenAI model documentationSolar Pro 4
$0.06 per 1M cached input tokens
Upstage Solar Pro 4 launch postDocumented inputs
GPT Realtime
Not sourced
Solar Pro 4
Not sourced
Documented outputs
GPT Realtime
Not sourced
Solar Pro 4
Not sourced
Provider availability
GPT Realtime
Not sourced
Solar Pro 4
Not sourced
Reasoning profile
GPT Realtime
Non-Reasoning
Solar Pro 4
Reasoning
Weight access
GPT Realtime
Proprietary
Solar Pro 4
Proprietary
License
GPT Realtime
Proprietary
Solar Pro 4
Proprietary
Release date
GPT Realtime
Not sourced
Solar Pro 4
2026-08-11
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: $0.248 vs $0.0186. Cache-heavy agent loop: $0.32 vs $0.03.
- Context tradeoff
- Solar Pro 4 has the larger documented window (512K).
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 evidence10 rows
Frequently asked questions
Which is better, GPT Realtime or Solar Pro 4?
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 or Solar Pro 4?
GPT Realtime and Solar Pro 4 are not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, GPT Realtime or Solar Pro 4?
GPT Realtime and Solar Pro 4 are not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, GPT Realtime or Solar Pro 4?
For the stated presets, chat costs $0.012 on GPT Realtime and $0.0009 on Solar Pro 4; repository review costs $0.248 and $0.0186; the cache-heavy agent loop costs $0.32 and $0.03. GPT Realtime does not fit this workload in one request.
Which has the larger context window, GPT Realtime or Solar Pro 4?
Solar Pro 4 has the larger documented context window: 512K, compared with 32K.
Related comparisons
Last updated September 10, 2026
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