Agentic
Not comparable- GPT-5.5
- 81.6
- Grok 4.3
- Not measured
- Weighted basis
- 3 vs 0 rows
- Reading
- Not comparable
Model comparison
GPT-5.5 vs Grok 4.3
Updated July 30, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
GPT-5.5 has the higher public score estimate, 72.04 versus 64.2, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
2 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.
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.
Chat turn cost
1K fresh input + 500 output tokens
Grok 4.3
Grok 4.3 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
Grok 4.3
Grok 4.3 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
Grok 4.3
Grok 4.3 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
No shared weighted benchmark basis supports a winner.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
Confidence: limited
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
31 GPT-5.5 only
2 Shared
- Shared results
- 2
- GPT-5.5 only
- 31
- Grok 4.3 only
- 0
- Like-for-like categories
- 0 / 8
Category results, on a stated basis
Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.
Coding
Not comparable- GPT-5.5
- 58.6
- Grok 4.3
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-5.5
- 85.0
- Grok 4.3
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- GPT-5.5
- 57.8
- Grok 4.3
- Not measured
- Weighted basis
- 2 vs 0 rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.5
- 47.6
- Grok 4.3
- Not measured
- Weighted basis
- 2 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.5
- Not measured
- Grok 4.3
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-5.5
- 70.4
- Grok 4.3
- Not measured
- Weighted basis
- 2 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-5.5
- Not measured
- Grok 4.3
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | GPT-5.5 | Grok 4.3 | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | 81.6 | Not measured | Not comparable3 vs 0 rows | Not comparable |
| Coding | 58.6 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Reasoning | 85.0 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Knowledge | 57.8 | Not measured | Not comparable2 vs 0 rows | Not comparable |
| Math | 47.6 | Not measured | Not comparable2 vs 0 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | 70.4 | Not measured | Not comparable2 vs 0 rows | Not comparable |
| Instruction following | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
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.
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-5.5
- $0.02
- Fits in one request
- Grok 4.3
- $0.0025
- Fits in one request
Grok 4.3 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- GPT-5.5
- $0.34
- Fits in one request
- Grok 4.3
- $0.07
- Fits in one request
Grok 4.3 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-5.5
- $0.5
- Fits in one request
- Grok 4.3
- $0.09
- Fits in one request
Grok 4.3 has the lower modeled cost
Costs use the listed standard API rates.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGPT-5.5Grok 4.3
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.5
Grok 4.3
1M
API model ID
GPT-5.5
gpt-5.5
OpenAI pricingGrok 4.3
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-5.5
$0.5 per 1M cached input tokens
OpenAI pricingGrok 4.3
$0.2 per 1M cached input tokens
Documented inputs
GPT-5.5
Not sourced
Grok 4.3
Not sourced
Documented outputs
GPT-5.5
Not sourced
Grok 4.3
Not sourced
Provider availability
GPT-5.5
Not sourced
Grok 4.3
Not sourced
Reasoning profile
GPT-5.5
Reasoning
Grok 4.3
Reasoning
Weight access
GPT-5.5
Proprietary
Grok 4.3
Proprietary
License
GPT-5.5
Proprietary
Grok 4.3
Proprietary
Release date
GPT-5.5
2026-04-23
Grok 4.3
2026-04-30
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
- GPT-5.5 has the higher public score estimate, 72.04 versus 64.2, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.34 vs $0.07. Cache-heavy agent loop: $0.5 vs $0.09.
- Context tradeoff
- Both models list 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 evidence33 rows
Agentic
Terminal-Bench 2.0
GPT-5.582%SourceGrok 4.3—Not directly comparable
CyberGym
GPT-5.581.8%SourceGrok 4.3—Not directly comparable
BrowseComp
GPT-5.584.4%SourceGrok 4.3—Not directly comparable
OSWorld-Verified
GPT-5.578.7%SourceGrok 4.3—Not directly comparable
MCP Atlas
GPT-5.575.3%SourceGrok 4.3—Not directly comparable
Toolathlon
GPT-5.555.6%SourceGrok 4.3—Not directly comparable
τ²-bench results
GPT-5.598%SourceGrok 4.3—Not directly comparable
Gert Labs
Shared sourceGPT-5.572.93%Grok 4.343.86%GPT-5.5 leads this result
ResearchClawBench
Shared sourceGPT-5.517.0%Grok 4.312.4%GPT-5.5 leads this result
OSWorld 2.0
GPT-5.513.0%SourceGrok 4.3—Not directly comparable
JobBench
GPT-5.542.7%SourceGrok 4.3—Not directly comparable
ExploitGym
GPT-5.513.4%SourceGrok 4.3—Not directly comparable
Coding
SWE-bench Pro
GPT-5.558.6%SourceGrok 4.3—Not directly comparable
Terminal-Bench 2.0
GPT-5.582.0%SourceGrok 4.3—Not directly comparable
Vibe Code Bench
GPT-5.569.85%SourceGrok 4.3—Not directly comparable
React Native Evals
GPT-5.584.7%SourceGrok 4.3—Not directly comparable
cursorBench31
GPT-5.559.2%SourceGrok 4.3—Not directly comparable
cursorBench32
GPT-5.558.4%SourceGrok 4.3—Not directly comparable
FrontierCode 1.1 Main
GPT-5.543.0%SourceGrok 4.3—Not directly comparable
Reasoning
Knowledge
Math
Multimodal
Frequently asked questions
Which is better, GPT-5.5 or Grok 4.3?
GPT-5.5 has the higher public score estimate, 72.04 versus 64.2, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, GPT-5.5 or Grok 4.3?
The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.
Which is better for agentic tasks, GPT-5.5 or Grok 4.3?
The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.
Which costs less, GPT-5.5 or Grok 4.3?
For the stated presets, chat costs $0.02 on GPT-5.5 and $0.0025 on Grok 4.3; repository review costs $0.34 and $0.07; the cache-heavy agent loop costs $0.5 and $0.09. Costs use the listed standard API rates.
Which has the larger context window, GPT-5.5 or Grok 4.3?
Both models list the same context window, 1M.
Related comparisons
Last updated July 30, 2026
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