Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Inworld Realtime TTS-2 Flash is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limited
GPT-5.2-Codex vs Inworld Realtime TTS-2 Flash
Updated November 13, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.
Share or export
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.
- API price per 1M tokens
GPT-5.2-Codex: $1.75 input / $14 output
Inworld Realtime TTS-2 Flash: No comparable API price
- Context window
GPT-5.2-Codex: 400K
Inworld Realtime TTS-2 Flash: N/A
- Released
GPT-5.2-Codex: December 18, 2025
Inworld Realtime TTS-2 Flash: August 31, 2026
- Shared results
- 0
- GPT-5.2-Codex only
- 6
- Inworld Realtime TTS-2 Flash only
- 0
- Like-for-like categories
- 0 / 8
Estimated: GPT-5.2-Codex. Conditional ranges do not establish rank confidence.How the comparison works
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.
No workload recommendation clears the current evidence threshold.
Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.
Show secondary and unsupported calls
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Inworld Realtime TTS-2 Flash is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limitedLong documents
Prompts that approach the documented context limit
Not enough matched evidence
A complete context comparison is not sourced.
Confidence: limitedChat 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-ratesCache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: rate-fallbackRepository review cost
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Which one for a specific job
Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
45.0GPT-5.2-Codex—Inworld Realtime TTS-2 Flash
Not comparable · BenchAlign v5.8
The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Same basis rules as the category table below
Or describe your own job
The LLM Selector reads what you need a model for and returns a shortlist, ranked in the same order the job pages publish.
Describe the task and constraints. This selector recommends models; it cannot carry out the task or access your files and accounts.
We keep what you type here, drafts included, for 180 days, and after that only the job and requirements read from it. Nothing is tied to your IP address or account. Leave out names and other personal details. Privacy
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
6 GPT-5.2-Codex only
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.
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign v5.8 lane for agentic, coding, reasoning, multimodal, 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.
Agentic
Not comparable- GPT-5.2-Codex
- 43.1
- Estimated · #67/135
- Inworld Realtime TTS-2 Flash
- Not ranked
- Basis
- BenchAlign v5.8 lane · 2 vs 0 public rows
- Reading
- Not comparable
Coding
Not comparable- GPT-5.2-Codex
- 45.0
- Supported · #60/162
- Inworld Realtime TTS-2 Flash
- Not ranked
- Basis
- BenchAlign v5.8 lane · 4 vs 0 public rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-5.2-Codex
- 43.6
- Estimated · #75/160
- Inworld Realtime TTS-2 Flash
- Not ranked
- Basis
- BenchAlign v5.8 lane · 0 vs 0 public rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-5.2-Codex
- Not ranked
- Inworld Realtime TTS-2 Flash
- Not ranked
- Basis
- BenchAlign v5.8 lane · 0 vs 0 public rows
- Reading
- Not comparable
Knowledge
Not comparable- GPT-5.2-Codex
- 57.1
- Estimated · #59/180
- Inworld Realtime TTS-2 Flash
- Not ranked
- Basis
- BenchAlign v5.8 lane · 0 vs 0 public rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.2-Codex
- Not ranked
- Inworld Realtime TTS-2 Flash
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-5.2-Codex
- 93.8
- #5/157
- Inworld Realtime TTS-2 Flash
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.2-Codex
- Not ranked
- Inworld Realtime TTS-2 Flash
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | GPT-5.2-Codex | Inworld Realtime TTS-2 Flash | Basis | Reading |
|---|---|---|---|---|
| Agentic | 43.1Estimated · #67/135 | Not ranked | Not comparableBenchAlign v5.8 lane · 2 vs 0 public rows | Not comparable |
| Coding | 45.0Supported · #60/162 | Not ranked | Not comparableBenchAlign v5.8 lane · 4 vs 0 public rows | Not comparable |
| Reasoning | 43.6Estimated · #75/160 | Not ranked | Not comparableBenchAlign v5.8 lane · 0 vs 0 public rows | Not comparable |
| Multimodal | Not ranked | Not ranked | Not comparableBenchAlign v5.8 lane · 0 vs 0 public rows | Not comparable |
| Knowledge | 57.1Estimated · #59/180 | Not ranked | Not comparableBenchAlign v5.8 lane · 0 vs 0 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 93.8#5/157 | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 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, reasoning, multimodal, and knowledge denominators (BenchAlign v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
Supported evidence per lane · bars run 0–100Methodology
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-5.2-Codex
- $0.00875
- Fits in one request
- Inworld Realtime TTS-2 Flash
- API rate not published
- Fit state unavailable
Inworld Realtime TTS-2 Flash has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- GPT-5.2-Codex
- $0.1295
- Fits in one request
- Inworld Realtime TTS-2 Flash
- API rate not published
- Fit state unavailable
Inworld Realtime TTS-2 Flash has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- GPT-5.2-Codex
- $0.525
- Fits in one request
- Cached input priced at the published list-input rate
- Inworld Realtime TTS-2 Flash
- API rate not published
- Fit state unavailable
- Cached-input rate unavailable
GPT-5.2-Codex has no published cached-input rate, so cached tokens use its listed input rate. Inworld Realtime TTS-2 Flash has no comparable published API token rate.
Cached input falls back to the list input rate only where a cached rate is unpublished
Price against score, among all priced models
Overall score against output price per 1M tokens, one model per family. Pricing as of November 12, 2026.
GPT-5.2-Codex
- 24 models score at least as high as GPT-5.2-Codex for the same price or less. The nearest is GPT-5.3 Codex (score 61.7, also $14.00 per 1M output tokens).
- Inworld Realtime TTS-2 Flash has no overall score yet, so it is not plotted.
Gray dots are the other priced models. The dark step line is the efficiency frontier: for a model under it, another model costs no more and scores at least as high.
See GPT-5.2-Codex on the full price vs performance chartSpecification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGPT-5.2-CodexInworld Realtime TTS-2 Flash
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.2-Codex
400K
Inworld Realtime TTS-2 Flash
N/A
API model ID
GPT-5.2-Codex
Not sourced
Inworld Realtime TTS-2 Flash
inworld-tts-2-flash
Inworld model documentationCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-5.2-Codex
Not published
Inworld Realtime TTS-2 Flash
No comparable hosted API rate
Inworld model documentationDocumented inputs
GPT-5.2-Codex
text, image
OpenAI model page for gpt-5.2-codexInworld Realtime TTS-2 Flash
Not sourced
Documented outputs
GPT-5.2-Codex
Inworld Realtime TTS-2 Flash
Not sourced
Provider availability
GPT-5.2-Codex
Not sourced
Inworld Realtime TTS-2 Flash
Not sourced
Reasoning profile
GPT-5.2-Codex
Reasoning
Inworld Realtime TTS-2 Flash
Non-Reasoning
Weight access
GPT-5.2-Codex
Proprietary
Inworld Realtime TTS-2 Flash
Proprietary
License
GPT-5.2-Codex
Proprietary
Inworld Realtime TTS-2 Flash
Proprietary
Release date
GPT-5.2-Codex
2025-12-18
Inworld Realtime TTS-2 Flash
2026-08-31
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
- A complete documented context comparison is not available.
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, GPT-5.2-Codex or Inworld Realtime TTS-2 Flash?
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-5.2-Codex or Inworld Realtime TTS-2 Flash?
Inworld Realtime TTS-2 Flash is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, GPT-5.2-Codex or Inworld Realtime TTS-2 Flash?
Inworld Realtime TTS-2 Flash is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, GPT-5.2-Codex or Inworld Realtime TTS-2 Flash?
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, GPT-5.2-Codex or Inworld Realtime TTS-2 Flash?
A complete documented context-window comparison is not available.
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
Coding
Vibe Code Bench
GPT-5.2-Codex37.91%SourceInworld Realtime TTS-2 Flash—Not directly comparable
LiveCodeBench (Vals)
GPT-5.2-Codex88.0%SourceInworld Realtime TTS-2 Flash—Not directly comparable
SWE-bench (Vals)
GPT-5.2-Codex72.4%SourceInworld Realtime TTS-2 Flash—Not directly comparable
PostTrainBench v1
GPT-5.2-Codex17.2%SourceInworld Realtime TTS-2 Flash—Not directly comparable
6 public results · 0 shared
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Last updated November 13, 2026