Chat turn cost
1K fresh input + 500 output tokens
GPT-5.1-Codex
GPT-5.1-Codex has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Gemini 4 Carbon vs GPT-5.1-Codex
Updated October 28, 2026. Rank says Gemini 4 Carbon is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
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
- Gemini 4 Carbon only
- 9
- GPT-5.1-Codex only
- 4
- Like-for-like categories
- 0 / 8
Estimated: Gemini 4 Carbon and GPT-5.1-Codex. Conditional ranges do not establish rank confidence.How 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.
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
GPT-5.1-Codex
GPT-5.1-Codex 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
Repository review cost
50K fresh input + 3K output tokens
GPT-5.1-Codex
GPT-5.1-Codex has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesCoding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
GPT-5.1-Codex is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Gemini 4 Carbon and GPT-5.1-Codex are not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limitedLong documents
Prompts that approach the documented context limit
Not enough matched evidence
A complete context comparison is not sourced.
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.
82.2Gemini 4 Carbon36.3GPT-5.1-Codex
Directional only · BenchAlign v5.8
Gemini 4 Carbon 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.
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.
9 Gemini 4 Carbon only
4 GPT-5.1-Codex only
2 categories rest 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 v5.8 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.
Coding
Directional only- Gemini 4 Carbon
- 82.2
- Supported · #3/156
- GPT-5.1-Codex
- 36.3
- Estimated · #79/156
- Basis
- BenchAlign v5.8 lane · 1 vs 2 public rows
- Reading
- Directional only
Knowledge
Directional only- Gemini 4 Carbon
- 88.2
- Supported · #2/178
- GPT-5.1-Codex
- 52.0
- Estimated · #68/178
- Basis
- BenchAlign v5.8 lane · 0 vs 0 public rows
- Reading
- Directional only
Agentic
Not comparable- Gemini 4 Carbon
- Not ranked
- GPT-5.1-Codex
- Not ranked
- Basis
- BenchAlign v5.8 lane · 4 vs 2 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Gemini 4 Carbon
- 79.5
- Unranked · 2 rankable rows
- GPT-5.1-Codex
- 72.6
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Gemini 4 Carbon
- Not ranked
- GPT-5.1-Codex
- 72.2
- Unranked · 1 rankable row
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Gemini 4 Carbon
- Not ranked
- GPT-5.1-Codex
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Gemini 4 Carbon
- Not ranked
- GPT-5.1-Codex
- 85.6
- #52/157
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Gemini 4 Carbon
- Not ranked
- GPT-5.1-Codex
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Gemini 4 Carbon | GPT-5.1-Codex | Basis | Reading |
|---|---|---|---|---|
| Coding | 82.2Supported · #3/156 | 36.3Estimated · #79/156 | Directional onlyBenchAlign v5.8 lane · 1 vs 2 public rows | Directional only |
| Knowledge | 88.2Supported · #2/178 | 52.0Estimated · #68/178 | Directional onlyBenchAlign v5.8 lane · 0 vs 0 public rows | Directional only |
| Agentic | Not ranked | Not ranked | Not comparableBenchAlign v5.8 lane · 4 vs 2 public rows | Not comparable |
| Reasoning | 79.5Unranked · 2 rankable rows | 72.6Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | 72.2Unranked · 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 | Not ranked | 85.6#52/157 | 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.
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
- Gemini 4 Carbon
- $0.007
- Fit state unavailable
- GPT-5.1-Codex
- $0.00625
- Fits in one request
GPT-5.1-Codex has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Gemini 4 Carbon
- $0.13
- Fit state unavailable
- GPT-5.1-Codex
- $0.0925
- Fits in one request
GPT-5.1-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
- Gemini 4 Carbon
- $0.16
- Fit state unavailable
- GPT-5.1-Codex
- $0.15
- Fits in one request
GPT-5.1-Codex has the lower modeled cost
Costs use the listed standard API rates.
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.
SpecificationGemini 4 CarbonGPT-5.1-Codex
Context window
Maximum documented context; output-token limits may be lower.
Gemini 4 Carbon
Not sourced
Google Gemini 4 Carbon launchGPT-5.1-Codex
API model ID
Gemini 4 Carbon
Not sourced
GPT-5.1-Codex
gpt-5.1-codex
OpenAI GPT-5.1-Codex model documentationCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Gemini 4 Carbon
$0.1 per 1M cached input tokens
Google Gemini 4 Carbon launch postGPT-5.1-Codex
$0.125 per 1M cached input tokens
OpenAI GPT-5.1-Codex model documentationDocumented inputs
Gemini 4 Carbon
Not sourced
GPT-5.1-Codex
Not sourced
Documented outputs
Gemini 4 Carbon
Not sourced
GPT-5.1-Codex
Not sourced
Provider availability
Gemini 4 Carbon
Limited Access · Fairwind Program
Google Gemini 4 Carbon launchGPT-5.1-Codex
Not sourced
Reasoning profile
Gemini 4 Carbon
Reasoning
GPT-5.1-Codex
Reasoning
Weight access
Gemini 4 Carbon
Proprietary
GPT-5.1-Codex
Proprietary
License
Gemini 4 Carbon
Proprietary
GPT-5.1-Codex
Proprietary
Release date
Gemini 4 Carbon
2026-10-28
GPT-5.1-Codex
2025-11-13
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
- Repository review: $0.13 vs $0.0925. Cache-heavy agent loop: $0.16 vs $0.15.
- Context tradeoff
- A complete documented context comparison is not available.
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Gemini 4 Carbon or GPT-5.1-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, Gemini 4 Carbon or GPT-5.1-Codex?
Gemini 4 Carbon scores higher for coding on the public lane, 82.2 to 36.3. GPT-5.1-Codex 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, Gemini 4 Carbon or GPT-5.1-Codex?
Gemini 4 Carbon and GPT-5.1-Codex are not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Gemini 4 Carbon or GPT-5.1-Codex?
For the stated presets, chat costs $0.007 on Gemini 4 Carbon and $0.00625 on GPT-5.1-Codex; repository review costs $0.13 and $0.0925; the cache-heavy agent loop costs $0.16 and $0.15. Costs use the listed standard API rates.
Which has the larger context window, Gemini 4 Carbon or GPT-5.1-Codex?
A complete documented context-window comparison is not available.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence13 rows
Agentic
AutomationBench
Gemini 4 Carbon53.9%SourceGPT-5.1-Codex—Not directly comparable
OSWorld 2.1 Offline
Gemini 4 Carbon81.5%SourceGPT-5.1-Codex—Not directly comparable
CWE-bench v1
Gemini 4 Carbon71.0%SourceGPT-5.1-Codex—Not directly comparable
ExploitGym
Gemini 4 Carbon53.5%SourceGPT-5.1-Codex—Not directly comparable
Gert Labs
Gemini 4 Carbon—GPT-5.1-Codex49.68%SourceNot directly comparable
JobBench
Gemini 4 Carbon—GPT-5.1-Codex26.2%SourceNot directly comparable
Coding
Reasoning
GraphWalks BFS 256K–1M
Gemini 4 Carbon96.2%SourceGPT-5.1-Codex—Not directly comparable
Multimodal
Instruction following
Gray Swan IPI (15 attempts)
Gemini 4 Carbon0.0%SourceGPT-5.1-Codex—Not directly comparable
13 public results · 0 shared
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Last updated October 28, 2026