Updated October 17, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. 1 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.
200K cached + 20K fresh input + 10K output tokens
GPT-5.1-Codex-Max
GPT-5.1-Codex-Max has the lower estimated token cost for this stated workload. GPT-5 (high) has no published cached-input rate, so cached tokens use its listed input rate.
Code generation, repair, and software-engineering tasks
Not enough matched evidence
GPT-5.1-Codex-Max and GPT-5 (high) are not ranked on the public lane for coding, so no winner is named for coding.
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GPT-5.1-Codex-Max and GPT-5 (high) are not ranked on the public lane for agentic, so no winner is named for agentic.
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
1K fresh input + 500 output tokens
No clear pick
A complete comparable API-rate estimate is not available for both models.
50K fresh input + 3K output tokens
No clear pick
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.
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.
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
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.
Each 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 | GPT-5.1-Codex-Max | GPT-5 (high) | Basis | Reading |
|---|---|---|---|---|
| Agentic | Not ranked | Not ranked | Not comparableBenchAlign v5.8 lane · 0 vs 1 public rows | Not comparable |
| Coding | Not ranked | Not ranked | Not comparableBenchAlign v5.8 lane · 2 vs 1 public rows | Not comparable |
| Reasoning | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | Not ranked | 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 | 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
Modeled costs are equal
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Modeled costs are equal
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
GPT-5.1-Codex-Max has the lower modeled cost
GPT-5 (high) has no published cached-input rate, so cached tokens use its listed input 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.
GPT-5.1-Codex-Max
GPT-5 (high)
400K
GPT-5.1-Codex-Max
Not sourced
GPT-5 (high)
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-5.1-Codex-Max
$0.125 per 1M cached input tokens
OpenAI GPT-5.1-Codex-Max model documentationGPT-5 (high)
Not published
GPT-5.1-Codex-Max
Not sourced
GPT-5 (high)
Not sourced
GPT-5.1-Codex-Max
Not sourced
GPT-5 (high)
Not sourced
GPT-5.1-Codex-Max
Not sourced
GPT-5 (high)
Not sourced
GPT-5.1-Codex-Max
Reasoning
GPT-5 (high)
Reasoning
GPT-5.1-Codex-Max
Proprietary
GPT-5 (high)
Proprietary
GPT-5.1-Codex-Max
Proprietary
GPT-5 (high)
Proprietary
GPT-5.1-Codex-Max
2025-11-19
GPT-5 (high)
2025-08-07
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.
GPT-5.1-Codex-Max and GPT-5 (high) are not ranked on the public lane for coding, so no winner is named for coding.
GPT-5.1-Codex-Max and GPT-5 (high) are not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
For the stated presets, chat costs $0.00625 on GPT-5.1-Codex-Max and $0.00625 on GPT-5 (high); repository review costs $0.0925 and $0.0925; the cache-heavy agent loop costs $0.15 and $0.375. GPT-5 (high) has no published cached-input rate, so cached tokens use its listed input rate.
Both models list the same context window, 400K.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
JobBench
Not directly comparable
Vibe Code Bench
Shared sourceGPT-5.1-Codex-Max leads this result
LiveCodeBench (Vals)
Not directly comparable
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Last updated October 17, 2026
