Agentic
Not comparable- Grok 4.3
- Not measured
- Kimi K2.5 (Reasoning)
- 55.0
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Model comparison
Updated July 30, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Grok 4.3 has the higher public score estimate, 64.2 versus 58.53, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
1 results are shared. Category rows based on 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.
Prompts that approach the documented context limit
Grok 4.3
Grok 4.3 has the larger documented context window.
Confidence: documented
1K fresh input + 500 output tokens
Kimi K2.5 (Reasoning)
Kimi K2.5 (Reasoning) has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
200K cached + 20K fresh input + 10K output tokens
Grok 4.3
Grok 4.3 has the lower estimated token cost for this stated workload. Kimi K2.5 (Reasoning) has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
50K fresh input + 3K output tokens
Kimi K2.5 (Reasoning)
Kimi K2.5 (Reasoning) has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Code generation, repair, and software-engineering tasks
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
Confidence: limited
Tool use, computer use, and multi-step task completion
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
Confidence: limited
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
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.
| Category | Grok 4.3 | Kimi K2.5 (Reasoning) | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | Not measured | 55.0 | Not comparable0 vs 2 rows | Not comparable |
| Coding | Not measured | 76.8 | Not comparable0 vs 1 rows | Not comparable |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Knowledge | Not measured | 87.2 | Not comparable0 vs 2 rows | Not comparable |
| Math | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | Not measured | 78.5 | Not comparable0 vs 1 rows | Not comparable |
| Instruction following | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
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.
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
Kimi K2.5 (Reasoning) has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Kimi K2.5 (Reasoning) has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
Grok 4.3 has the lower modeled cost
Kimi K2.5 (Reasoning) 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.
Grok 4.3
1M
Kimi K2.5 (Reasoning)
256K
Grok 4.3
Not sourced
Kimi K2.5 (Reasoning)
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Grok 4.3
$0.2 per 1M cached input tokens
Kimi K2.5 (Reasoning)
Not published
Grok 4.3
Not sourced
Kimi K2.5 (Reasoning)
Not sourced
Grok 4.3
Not sourced
Kimi K2.5 (Reasoning)
Not sourced
Grok 4.3
Not sourced
Kimi K2.5 (Reasoning)
Not sourced
Grok 4.3
Reasoning
Kimi K2.5 (Reasoning)
Reasoning
Grok 4.3
Proprietary
Kimi K2.5 (Reasoning)
Proprietary
Grok 4.3
Proprietary
Kimi K2.5 (Reasoning)
Proprietary
Grok 4.3
2026-04-30
Kimi K2.5 (Reasoning)
2026-02-01
Run the same representative tasks against both endpoints before changing production traffic.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Gert Labs
Shared sourceGrok 4.3 leads this result
ResearchClawBench
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
BrowseComp
Not directly comparable
AIME 2025
Not directly comparable
MMMU-Pro
Not directly comparable
Grok 4.3 has the higher public score estimate, 64.2 versus 58.53, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.
The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.
For the stated presets, chat costs $0.0025 on Grok 4.3 and $0.0021 on Kimi K2.5 (Reasoning); repository review costs $0.07 and $0.039; the cache-heavy agent loop costs $0.09 and $0.162. Kimi K2.5 (Reasoning) has no published cached-input rate, so cached tokens use its listed input rate.
Grok 4.3 has the larger documented context window: 1M, compared with 256K.
Last updated July 30, 2026
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