Updated October 6, 2026. Rank says GPT-5.3 Codex 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
GPT-5.3 Codex has the higher public point estimate, 60.85 versus 40.49. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. 2 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
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.
Code generation, repair, and software-engineering tasks
GPT-5.3 Codex
GPT-5.3 Codex has the higher public coding point estimate, 55.8 to 33.2, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Prompts that approach the documented context limit
GPT-5.3 Codex
GPT-5.3 Codex has the larger documented context window.
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Gemma 4 31B and GPT-5.3 Codex are scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
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.
Like-for-like · BenchAlign v5.8
GPT-5.3 Codex has the higher coding point estimate. Conditional score ranges do not establish rank confidence.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
3 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
SWE-RebenchCoding
Normalized gap 16.6Each row shows the public-lane category score for both models: the BenchAlign v5.8 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.
| Category | Gemma 4 31B | GPT-5.3 Codex | Basis | Reading |
|---|---|---|---|---|
| Coding | 33.2Supported · #85/144 | 55.8Supported · #28/144 | Like-for-likeBenchAlign v5.8 lane · 2 vs 6 public rows | GPT-5.3 Codex leads · intervals overlap |
| Agentic | 19.1Estimated · #101/120 | 55.2Estimated · #34/120 | Directional onlyBenchAlign v5.8 lane · 1 vs 4 public rows | Directional only |
| Knowledge | 38.6Supported · #107/173 | 64.3Estimated · #27/173 | Directional onlyBenchAlign v5.8 lane · 4 vs 0 public rows | Directional only |
| Instruction following | 91.5#13/125 | 91.2#14/125 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Reasoning | 70.2Unranked · 2 rankable rows | 79.6Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 59.5#31/49 | 76.7Unranked · 1 rankable row | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | 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 v5.8) 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.
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.
1K fresh input + 500 output tokens
Gemma 4 31B has no comparable published API token rate.
50K fresh input + 3K output tokens
Gemma 4 31B has no comparable published API token rate.
200K cached + 20K fresh input + 10K output tokens
GPT-5.3 Codex has no published cached-input rate, so cached tokens use its listed input rate. Gemma 4 31B has no comparable published API token rate.
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
Maximum documented context; output-token limits may be lower.
Gemma 4 31B
GPT-5.3 Codex
Gemma 4 31B
gemma-4-31b-it
Google Gemma Gemini API guideGPT-5.3 Codex
gpt-5.3-codex
OpenAI GPT-5.3 Codex model documentationA missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Gemma 4 31B
No comparable hosted API rate
GPT-5.3 Codex
Not published
Gemma 4 31B
text, image
Google Gemma 4 model documentationGPT-5.3 Codex
text, image
OpenAI model catalogGemma 4 31B
GPT-5.3 Codex
Gemma 4 31B
Generally Available · Gemini API, Google AI Studio, open weights
Google Gemma Gemini API guideGPT-5.3 Codex
Generally Available · OpenAI Responses API
OpenAI model catalogGemma 4 31B
Reasoning
GPT-5.3 Codex
Reasoning
Gemma 4 31B
Open Weight
GPT-5.3 Codex
Proprietary
Gemma 4 31B
Open Weight
GPT-5.3 Codex
Proprietary
Gemma 4 31B
2026-04-02
GPT-5.3 Codex
2026-02-05
GPT-5.3 Codex has the higher public point estimate, 60.85 versus 40.49. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.
GPT-5.3 Codex has the higher public coding point estimate, 55.8 to 33.2, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
GPT-5.3 Codex scores higher for agentic tasks on the public lane, 55.2 to 19.1. Gemma 4 31B and GPT-5.3 Codex 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.
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
GPT-5.3 Codex has the larger documented context window: 400K, compared with 256K.
Estimates at 50,000 req/day · 1000 tokens/req average.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Gert Labs
Shared sourceGPT-5.3 Codex leads this result
Terminal-Bench 2.0
Not directly comparable
OSWorld-Verified
Not directly comparable
JobBench
Not directly comparable
SWE-Rebench
GPT-5.3 Codex leads this result
React Native Evals
Not directly comparable
SWE-bench Verified
Not directly comparable
SWE-bench Pro
Not directly comparable
Vibe Code Bench
Not directly comparable
LiveCodeBench (Vals)
Not directly comparable
SWE-bench (Vals)
Not directly comparable
MMMU-Pro
Not directly comparable
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Last updated October 6, 2026
