Agentic
Not comparable- Cosmos3-Edge
- Not ranked
- GPT-5.3 Codex
- 62.3
- Estimated · #19/151
- Basis
- BenchAlign lane · 0 vs 4 public rows
- Reading
- Not comparable
Cosmos3-Edge vs GPT-5.3 Codex
Updated September 4, 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
GPT-5.3 Codex
GPT-5.3 Codex 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
Cosmos3-Edge is 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
Cosmos3-Edge is not ranked on the public lane for agentic, so no winner is named for agentic.
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
A complete comparable API-rate estimate is not available for both models.
Confidence: rate-fallback
Repository 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
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
8 GPT-5.3 Codex only
- Shared results
- 0
- Cosmos3-Edge only
- 0
- GPT-5.3 Codex only
- 8
- 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- Cosmos3-Edge
- Not ranked
- GPT-5.3 Codex
- 62.7
- Supported · #16/183
- Basis
- BenchAlign lane · 0 vs 4 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Cosmos3-Edge
- Not ranked
- GPT-5.3 Codex
- 77.3
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- Cosmos3-Edge
- Not ranked
- GPT-5.3 Codex
- 66.3
- Estimated · #22/181
- Basis
- BenchAlign lane · 0 vs 0 public rows
- Reading
- Not comparable
Math
Not comparable- Cosmos3-Edge
- Not ranked
- GPT-5.3 Codex
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Cosmos3-Edge
- Not ranked
- GPT-5.3 Codex
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Cosmos3-Edge
- Not ranked
- GPT-5.3 Codex
- 75.4
- Unranked · 1 rankable row
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Cosmos3-Edge
- Not ranked
- GPT-5.3 Codex
- 92.3
- #15/120
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Cosmos3-Edge | GPT-5.3 Codex | Basis | Reading |
|---|---|---|---|---|
| Agentic | Not ranked | 62.3Estimated · #19/151 | Not comparableBenchAlign lane · 0 vs 4 public rows | Not comparable |
| Coding | Not ranked | 62.7Supported · #16/183 | Not comparableBenchAlign lane · 0 vs 4 public rows | Not comparable |
| Reasoning | Not ranked | 77.3Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | Not ranked | 66.3Estimated · #22/181 | Not comparableBenchAlign lane · 0 vs 0 public rows | Not comparable |
| Math | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | 75.4Unranked · 1 rankable row | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 92.3#15/120 | 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
- Cosmos3-Edge
- Self-hosted; infrastructure cost varies
- Fits in one request
- GPT-5.3 Codex
- $0.00875
- 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-5.3 Codex
- $0.1295
- Fits 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-5.3 Codex
- $0.525
- Fits in one request
- Cached input priced at the published list-input rate
GPT-5.3 Codex has no published cached-input rate, so cached tokens use its listed input rate. 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.
SpecificationCosmos3-EdgeGPT-5.3 Codex
Context window
Maximum documented context; output-token limits may be lower.
Cosmos3-Edge
256K
GPT-5.3 Codex
API model ID
Cosmos3-Edge
Not sourced
GPT-5.3 Codex
gpt-5.3-codex
OpenAI GPT-5.3 Codex model documentationCached-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-5.3 Codex
Not published
Documented inputs
Cosmos3-Edge
Not sourced
GPT-5.3 Codex
text, image
OpenAI model catalogDocumented outputs
Cosmos3-Edge
Not sourced
GPT-5.3 Codex
Provider availability
Cosmos3-Edge
Not sourced
GPT-5.3 Codex
Generally Available · OpenAI Responses API
OpenAI model catalogReasoning profile
Cosmos3-Edge
Reasoning
GPT-5.3 Codex
Reasoning
Weight access
Cosmos3-Edge
Open Weight
GPT-5.3 Codex
Proprietary
License
Cosmos3-Edge
Open Weight
GPT-5.3 Codex
Proprietary
Release date
Cosmos3-Edge
2026-07-20
GPT-5.3 Codex
2026-02-05
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
- GPT-5.3 Codex has the larger documented window (400K).
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 evidence8 rows
Agentic
Coding
SWE-bench Verified
Cosmos3-Edge—GPT-5.3 Codex85%SourceNot directly comparable
SWE-bench Pro
Cosmos3-Edge—GPT-5.3 Codex56.8%SourceNot directly comparable
SWE-Rebench
Cosmos3-Edge—GPT-5.3 Codex58.2%SourceNot directly comparable
Vibe Code Bench
Cosmos3-Edge—GPT-5.3 Codex61.77%SourceNot directly comparable
Frequently asked questions
Which is better, Cosmos3-Edge or GPT-5.3 Codex?
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-5.3 Codex?
Cosmos3-Edge is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, Cosmos3-Edge or GPT-5.3 Codex?
Cosmos3-Edge is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Cosmos3-Edge or GPT-5.3 Codex?
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-5.3 Codex?
GPT-5.3 Codex has the larger documented context window: 400K, compared with 256K.
Related comparisons
Last updated September 4, 2026
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