Coding work
Code generation, repair, and software-engineering tasks
GPT-6.1 Sol
GPT-6.1 Sol leads on the public coding lane, 67 to 55.5, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
GPT-5.3 Codex vs GPT-6.1 Sol
Updated September 29, 2026. Rank says GPT-6.1 Sol is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
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.
- Shared results
- 0
- GPT-5.3 Codex only
- 10
- GPT-6.1 Sol only
- 9
- Like-for-like categories
- 1 / 8
Supported: GPT-5.3 Codex · Estimated: GPT-6.1 SolHow 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.
Long documents
Prompts that approach the documented context limit
GPT-6.1 Sol
GPT-6.1 Sol has the larger documented context window.
Confidence: documentedChat turn cost
1K fresh input + 500 output tokens
GPT-6.1 Sol
GPT-6.1 Sol 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
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
GPT-6.1 Sol
GPT-6.1 Sol 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-fallbackRepository review cost
50K fresh input + 3K output tokens
GPT-5.3 Codex
GPT-5.3 Codex has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GPT-6.1 Sol is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited
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.
55.5GPT-5.3 Codex67.0GPT-6.1 Sol
Like-for-like · BenchAlign v5.7
GPT-6.1 Sol leads the like-for-like coding row, although the 90% intervals overlap.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Same basis rules as the category table below
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
10 GPT-5.3 Codex only
9 GPT-6.1 Sol only
1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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 v5.7 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
Like-for-like- GPT-5.3 Codex
- 55.5
- Supported · #29/143
- GPT-6.1 Sol
- 67.0
- Supported · #8/143
- Basis
- BenchAlign v5.7 lane · 6 vs 1 public rows
- Reading
- GPT-6.1 Sol leads · intervals overlap
Knowledge
Directional only- GPT-5.3 Codex
- 63.8
- Estimated · #31/169
- GPT-6.1 Sol
- 71.3
- Estimated · #10/169
- Basis
- BenchAlign v5.7 lane · 0 vs 5 public rows
- Reading
- Directional only
Agentic
Not comparable- GPT-5.3 Codex
- 54.1
- Estimated · #34/117
- GPT-6.1 Sol
- Not ranked
- Basis
- BenchAlign v5.7 lane · 4 vs 3 public rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-5.3 Codex
- 79.6
- Unranked · 2 rankable rows
- GPT-6.1 Sol
- 79.4
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-5.3 Codex
- 76.7
- Unranked · 1 rankable row
- GPT-6.1 Sol
- 83.9
- Unranked · 1 rankable row
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.3 Codex
- Not ranked
- GPT-6.1 Sol
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-5.3 Codex
- 91.2
- #14/124
- GPT-6.1 Sol
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.3 Codex
- Not ranked
- GPT-6.1 Sol
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | GPT-5.3 Codex | GPT-6.1 Sol | Basis | Reading |
|---|---|---|---|---|
| Coding | 55.5Supported · #29/143 | 67.0Supported · #8/143 | Like-for-likeBenchAlign v5.7 lane · 6 vs 1 public rows | GPT-6.1 Sol leads · intervals overlap |
| Knowledge | 63.8Estimated · #31/169 | 71.3Estimated · #10/169 | Directional onlyBenchAlign v5.7 lane · 0 vs 5 public rows | Directional only |
| Agentic | 54.1Estimated · #34/117 | Not ranked | Not comparableBenchAlign v5.7 lane · 4 vs 3 public rows | Not comparable |
| Reasoning | 79.6Unranked · 2 rankable rows | 79.4Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 76.7Unranked · 1 rankable row | 83.9Unranked · 1 rankable row | 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 |
| Instruction following | 91.2#14/124 | 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, and knowledge denominators (BenchAlign v5.7) 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-5.3 Codex
- $0.00875
- Fits in one request
- GPT-6.1 Sol
- $0.007
- Fits in one request
GPT-6.1 Sol 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
- GPT-6.1 Sol
- $0.13
- Fits in one request
GPT-5.3 Codex 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
- GPT-6.1 Sol
- $0.16
- Fits in one request
GPT-6.1 Sol has the lower modeled cost
GPT-5.3 Codex has no published cached-input rate, so cached tokens use its listed input rate.
Cached input falls back to the list input rate only where a cached rate is unpublished
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGPT-5.3 CodexGPT-6.1 Sol
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.3 Codex
GPT-6.1 Sol
API model ID
GPT-5.3 Codex
gpt-5.3-codex
OpenAI GPT-5.3 Codex model documentationGPT-6.1 Sol
gpt-6.1-sol
OpenAI GPT-6.1 Sol model documentationCached-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
GPT-6.1 Sol
$0.1 per 1M cached input tokens
OpenAI GPT-6.1 Sol model documentationDocumented inputs
GPT-5.3 Codex
text, image
OpenAI model catalogGPT-6.1 Sol
text, image
OpenAI GPT-6.1 Sol model documentationDocumented outputs
GPT-5.3 Codex
GPT-6.1 Sol
Provider availability
GPT-5.3 Codex
Generally Available · OpenAI Responses API
OpenAI model catalogGPT-6.1 Sol
Generally Available · OpenAI Responses API, OpenAI Chat Completions API
OpenAI GPT-6.1 Sol model documentationReasoning profile
GPT-5.3 Codex
Reasoning
GPT-6.1 Sol
Reasoning
Weight access
GPT-5.3 Codex
Proprietary
GPT-6.1 Sol
Proprietary
License
GPT-5.3 Codex
Proprietary
GPT-6.1 Sol
Proprietary
Release date
GPT-5.3 Codex
2026-02-05
GPT-6.1 Sol
2026-09-29
If you already use one of these models
- Deployment change
- Both entries list OpenAI as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
- 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: $0.1295 vs $0.13. Cache-heavy agent loop: $0.525 vs $0.16.
- Context tradeoff
- GPT-6.1 Sol has the larger documented window (1.05M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, GPT-5.3 Codex or GPT-6.1 Sol?
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-5.3 Codex or GPT-6.1 Sol?
GPT-6.1 Sol leads the public coding lane, 67 to 55.5, with Supported evidence for both models, although the 90% intervals overlap.
Which is better for agentic tasks, GPT-5.3 Codex or GPT-6.1 Sol?
GPT-6.1 Sol is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, GPT-5.3 Codex or GPT-6.1 Sol?
For the stated presets, chat costs $0.00875 on GPT-5.3 Codex and $0.007 on GPT-6.1 Sol; repository review costs $0.1295 and $0.13; the cache-heavy agent loop costs $0.525 and $0.16. 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 GPT-6.1 Sol?
GPT-6.1 Sol has the larger documented context window: 1.05M, compared with 400K.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence19 rows
Agentic
Terminal-Bench 2.0
GPT-5.3 Codex77.3%SourceGPT-6.1 Sol—Not directly comparable
OSWorld-Verified
GPT-5.3 Codex64.7%SourceGPT-6.1 Sol—Not directly comparable
Gert Labs
GPT-5.3 Codex57.47%SourceGPT-6.1 Sol—Not directly comparable
JobBench
GPT-5.3 Codex33.7%SourceGPT-6.1 Sol—Not directly comparable
AutomationBench
GPT-5.3 Codex—GPT-6.1 Sol36.1%SourceNot directly comparable
Terminal-Bench-Science 0.1
GPT-5.3 Codex—GPT-6.1 Sol57.0%SourceNot directly comparable
ExploitGym
GPT-5.3 Codex—GPT-6.1 Sol35.1%SourceNot directly comparable
Coding
SWE-bench Verified
GPT-5.3 Codex85%SourceGPT-6.1 Sol—Not directly comparable
SWE-bench Pro
GPT-5.3 Codex56.8%SourceGPT-6.1 Sol—Not directly comparable
SWE-Rebench
GPT-5.3 Codex58.2%SourceGPT-6.1 Sol—Not directly comparable
Vibe Code Bench
GPT-5.3 Codex61.77%SourceGPT-6.1 Sol—Not directly comparable
LiveCodeBench (Vals)
GPT-5.3 Codex87.3%SourceGPT-6.1 Sol—Not directly comparable
SWE-bench (Vals)
GPT-5.3 Codex78.0%SourceGPT-6.1 Sol—Not directly comparable
DeepSWE
GPT-5.3 Codex—GPT-6.1 Sol71.9%SourceNot directly comparable
Knowledge
HealthBench (raw)
GPT-5.3 Codex—GPT-6.1 Sol56.7%SourceNot directly comparable
HealthBench (length-adjusted)
GPT-5.3 Codex—GPT-6.1 Sol58.5%SourceNot directly comparable
HealthBench Professional
GPT-5.3 Codex—GPT-6.1 Sol64.2%SourceNot directly comparable
HealthBench Professional (raw)
GPT-5.3 Codex—GPT-6.1 Sol67.2%SourceNot directly comparable
HealthBench Hard
GPT-5.3 Codex—GPT-6.1 Sol36.2%SourceNot directly comparable
19 public results · 0 shared
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Last updated September 29, 2026