AI Benchmark Comparison: GPT‑5.5, Claude Opus 4.7, Gemini 3.5 Flash, Grok 4.3, and DeepSeek V4

Claude Opus 4.7, meanwhile, shines in real‑world coding benchmarks. Anthropic reports 64.3% on SWE‑Bench Pro and 87.6% on SWE‑Bench Verified, which measure whether a model can fix real issues in open‑source GitHub repositories.

Google’s Gemini 3.5 Flash stands out because it performs unusually well for a model optimized for speed. In Google’s published cross‑vendor table, it scores 76.2% on Terminal‑Bench 2.1, compared with 78.2% for GPT‑5.5 and 66.1% for Claude Opus 4.7 on that benchmark version.

By contrast, Grok 4.3 and DeepSeek V4 are harder to position precisely due to differences in transparency and benchmark methodology.

Coding benchmarks

Software engineering performance is one of the clearest areas where models diverge.

Claude Opus 4.7 currently has the strongest public signal in this category. Its 64.3% score on SWE‑Bench Pro represents a significant improvement over earlier models and suggests strong performance resolving real‑world GitHub issues across multiple programming languages.

GPT‑5.5 scores slightly lower on that specific benchmark at 58.6%, but it performs extremely well on broader engineering workflows. For example, Terminal‑Bench 2.0, which evaluates command‑line automation and multi‑tool coordination, shows GPT‑5.5 leading with 82.7%.

Gemini 3.5 Flash achieves 55.1% on SWE‑Bench Pro. That is lower than Opus 4.7 but notable for a model designed primarily for high‑speed inference.

Public coding benchmarks for Grok 4.3 are less standardized. Reported figures include 81% on IFBench and 98% on τ²‑Bench telecom tasks, though these evaluations measure narrower capabilities and are not directly comparable with SWE‑Bench or Terminal‑Bench.

For DeepSeek V4, independently verified coding benchmarks remain limited. Several widely cited numbers originate from internal testing or leaks that have not yet been reproduced externally.

Agent workflows and tool use

Modern AI benchmarks increasingly evaluate how well models coordinate tools, APIs, and multi‑step workflows—often called agentic capabilities.

Google reports that Gemini 3.5 Flash leads several tool‑use benchmarks, including 83.6% on MCP Atlas and 56.5% on Toolathlon, both designed to test how reliably a model orchestrates multiple tools to solve tasks.

OpenAI’s GPT‑5.5 performs strongly in similar scenarios through benchmarks such as GDPval, which measures knowledge‑work tasks across dozens of professional domains and shows 84.9% wins or ties against competing models.

Claude Opus 4.7 also performs well in computer‑interaction benchmarks. Its 78.0% score on OSWorld‑Verified indicates strong performance when operating desktop interfaces and interacting with software applications.

Context length, speed, and cost

Benchmarks only tell part of the story. Deployment characteristics—such as context window size, speed, and price—can strongly influence which model is most useful in practice.

Grok 4.3 emphasizes long‑context processing and cost efficiency. xAI documentation lists a 1‑million‑token context window, with pricing around $1.25 per million input tokens and $2.50 per million output tokens, positioning it as a lower‑cost option for large‑context workloads.

Gemini 3.5 Flash focuses on high‑speed inference and is frequently described as significantly faster than frontier models while remaining competitive across agentic benchmarks.

DeepSeek’s models generally emphasize open‑weight or low‑cost deployment strategies, making them attractive to organizations that want to run powerful models locally or on custom infrastructure.

Independent evaluation of DeepSeek V4

The most credible independent analysis of DeepSeek V4 comes from the U.S. National Institute of Standards and Technology (NIST) through its CAISI evaluation program.

According to that assessment, DeepSeek V4 is the most capable Chinese AI model evaluated across domains such as software engineering, cyber tasks, and mathematics, but lags leading frontier models by roughly eight months in capability.

The report also notes that DeepSeek’s internal benchmark claims appear stronger than CAISI’s independent measurements, highlighting the importance of neutral testing when comparing models across companies.

Why cross‑model comparisons are difficult

Even with public benchmark tables, comparing models directly remains imperfect for several reasons:

• Benchmarks often appear in different versions (for example Terminal‑Bench 2.0 vs 2.1).
• Some results come from vendor‑run evaluations rather than independent test suites.
• Composite indices and Elo scores (such as GDPval‑AA) cannot be compared directly with percentage‑based benchmarks.

Because of these differences, any strict ranking across all models should be interpreted cautiously.

What the evidence suggests right now

Looking at the strongest available public evidence:

• GPT‑5.5 appears to be the most broadly capable model across reasoning, knowledge work, and agentic tasks.
• Claude Opus 4.7 shows the clearest advantage in real‑world coding benchmarks like SWE‑Bench.
• Gemini 3.5 Flash is unusually powerful for a fast inference model and competes closely with flagship systems in several evaluations.
• Grok 4.3 offers impressive context length and promising composite metrics but fewer standardized benchmark comparisons.
• DeepSeek V4 is the strongest independently evaluated Chinese model but still trails current frontier systems according to NIST analysis.

In practice, the “best” AI model depends heavily on the workload. Coding agents, research assistants, large‑context analysis, and cost‑sensitive deployments may each favor a different system—even when headline benchmark scores look similar.