Agentic
Directional only- GPT-5.3 Codex
- 62.3
- Estimated · #19/151
- Laguna S 2.1
- 48.2
- Estimated · #77/151
- Basis
- BenchAlign lane · 4 vs 2 public rows
- Reading
- Directional only
GPT-5.3 Codex vs Laguna S 2.1
Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
2 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
Laguna S 2.1
Laguna S 2.1 has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
Laguna S 2.1
Laguna S 2.1 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Laguna S 2.1
Laguna S 2.1 has the lower estimated token cost for this stated workload. GPT-5.3 Codex has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
Show secondary and unsupported calls
Repository review cost
50K fresh input + 3K output tokens
Laguna S 2.1
Laguna S 2.1 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
Laguna S 2.1 is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GPT-5.3 Codex and Laguna S 2.1 are scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limited
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-5.3 Codex only
2 Shared
4 Laguna S 2.1 only
- Shared results
- 2
- GPT-5.3 Codex only
- 6
- Laguna S 2.1 only
- 4
- Like-for-like categories
- 0 / 8
2 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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
Directional only- GPT-5.3 Codex
- 62.7
- Supported · #16/183
- Laguna S 2.1
- 47.8
- Estimated · #87/183
- Basis
- BenchAlign lane · 4 vs 4 public rows
- Reading
- Directional only
Reasoning
Not comparable- GPT-5.3 Codex
- 77.3
- Unranked · 2 rankable rows
- Laguna S 2.1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- GPT-5.3 Codex
- 66.3
- Estimated · #22/181
- Laguna S 2.1
- Not ranked
- Basis
- BenchAlign lane · 0 vs 0 public rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.3 Codex
- Not ranked
- Laguna S 2.1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.3 Codex
- Not ranked
- Laguna S 2.1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-5.3 Codex
- 75.4
- Unranked · 1 rankable row
- Laguna S 2.1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-5.3 Codex
- 92.3
- #15/120
- Laguna S 2.1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | GPT-5.3 Codex | Laguna S 2.1 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 62.3Estimated · #19/151 | 48.2Estimated · #77/151 | Directional onlyBenchAlign lane · 4 vs 2 public rows | Directional only |
| Coding | 62.7Supported · #16/183 | 47.8Estimated · #87/183 | Directional onlyBenchAlign lane · 4 vs 4 public rows | Directional only |
| Reasoning | 77.3Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | 66.3Estimated · #22/181 | Not ranked | 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 | 75.4Unranked · 1 rankable row | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 92.3#15/120 | 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.
Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.
Terminal-Bench 2.0
Agentic
SWE-bench Pro
Coding
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-5.3 Codex
- $0.00875
- Fits in one request
- Laguna S 2.1
- $0.0002
- Fits in one request
Laguna S 2.1 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- GPT-5.3 Codex
- $0.1295
- Fits in one request
- Laguna S 2.1
- $0.0056
- Fits in one request
Laguna S 2.1 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-5.3 Codex
- $0.525
- Fits in one request
- Cached input priced at the published list-input rate
- Laguna S 2.1
- $0.006
- Fits in one request
Laguna S 2.1 has the lower modeled cost
GPT-5.3 Codex 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-5.3 CodexLaguna S 2.1
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.3 Codex
Laguna S 2.1
1M
API model ID
GPT-5.3 Codex
gpt-5.3-codex
OpenAI GPT-5.3 Codex model documentationLaguna S 2.1
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-5.3 Codex
Not published
Laguna S 2.1
$0.01 per 1M cached input tokens
Documented inputs
GPT-5.3 Codex
text, image
OpenAI model catalogLaguna S 2.1
Not sourced
Documented outputs
GPT-5.3 Codex
Laguna S 2.1
Not sourced
Provider availability
GPT-5.3 Codex
Generally Available · OpenAI Responses API
OpenAI model catalogLaguna S 2.1
Not sourced
Reasoning profile
GPT-5.3 Codex
Reasoning
Laguna S 2.1
Reasoning
Weight access
GPT-5.3 Codex
Proprietary
Laguna S 2.1
Open Weight
License
GPT-5.3 Codex
Proprietary
Laguna S 2.1
Open Weight
Release date
GPT-5.3 Codex
2026-02-05
Laguna S 2.1
2026-07-21
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
- At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
- Workload cost
- Repository review: $0.1295 vs $0.0056. Cache-heavy agent loop: $0.525 vs $0.006.
- Context tradeoff
- Laguna S 2.1 has the larger documented window (1M).
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 evidence12 rows
Agentic
Terminal-Bench 2.0
GPT-5.3 Codex77.3%SourceLaguna S 2.170.2%SourceGPT-5.3 Codex leads this result
OSWorld-Verified
GPT-5.3 Codex64.7%SourceLaguna S 2.1—Not directly comparable
Gert Labs
GPT-5.3 Codex57.47%SourceLaguna S 2.1—Not directly comparable
JobBench
GPT-5.3 Codex33.7%SourceLaguna S 2.1—Not directly comparable
Toolathlon-Verified
GPT-5.3 Codex—Laguna S 2.149.7%SourceNot directly comparable
Coding
SWE-bench Verified
GPT-5.3 Codex85%SourceLaguna S 2.1—Not directly comparable
SWE-bench Pro
GPT-5.3 Codex56.8%SourceLaguna S 2.159.4%SourceLaguna S 2.1 leads this result
SWE-Rebench
GPT-5.3 Codex58.2%SourceLaguna S 2.1—Not directly comparable
Vibe Code Bench
GPT-5.3 Codex61.77%SourceLaguna S 2.1—Not directly comparable
Terminal-Bench 2.0
GPT-5.3 Codex—Laguna S 2.170.2%SourceNot directly comparable
SWE Multilingual
GPT-5.3 Codex—Laguna S 2.178.5%SourceNot directly comparable
deepSwe
GPT-5.3 Codex—Laguna S 2.140.4%SourceNot directly comparable
Frequently asked questions
Which is better, GPT-5.3 Codex or Laguna S 2.1?
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, GPT-5.3 Codex or Laguna S 2.1?
GPT-5.3 Codex scores higher for coding on the public lane, 62.7 to 47.8. Laguna S 2.1 is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which is better for agentic tasks, GPT-5.3 Codex or Laguna S 2.1?
GPT-5.3 Codex scores higher for agentic tasks on the public lane, 62.3 to 48.2. GPT-5.3 Codex and Laguna S 2.1 are scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which costs less, GPT-5.3 Codex or Laguna S 2.1?
For the stated presets, chat costs $0.00875 on GPT-5.3 Codex and $0.0002 on Laguna S 2.1; repository review costs $0.1295 and $0.0056; the cache-heavy agent loop costs $0.525 and $0.006. GPT-5.3 Codex has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, GPT-5.3 Codex or Laguna S 2.1?
Laguna S 2.1 has the larger documented context window: 1M, compared with 400K.
Related comparisons
Last updated September 4, 2026
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