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

Claude Opus 4.7, however, stands out in real‑world software engineering benchmarks. Anthropic reports 64.3% on SWE‑Bench Pro and 87.6% on SWE‑Bench Verified, both benchmarks measuring whether a model can fix real issues in open‑source repositories.

Google’s Gemini 3.5 Flash is notable because it sits much closer to flagship models than typical “fast inference” tiers. In Google’s cross‑vendor benchmark 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.

Grok 4.3 and DeepSeek V4 are harder to rank precisely due to differences in evaluation transparency and methodology.

Coding benchmarks

Coding performance is one of the clearest areas of differentiation among frontier models.

Claude Opus 4.7 leads the strongest public signal here. Its 64.3% score on SWE‑Bench Pro represents a large improvement over earlier models and indicates strong performance resolving real GitHub issues across multiple programming languages.

OpenAI’s GPT‑5.5 performs slightly lower on that benchmark at 58.6%, but it performs extremely well on broader engineering tasks such as terminal‑based workflows. For example, Terminal‑Bench 2.0 measures complex command‑line automation and tool coordination, where GPT‑5.5 leads with 82.7%.

Gemini 3.5 Flash reaches 55.1% on SWE‑Bench Pro, a modest result compared with Opus 4.7 but notable for a fast‑tier model.

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

For DeepSeek V4, publicly verified coding benchmarks remain limited. Some claims originate from internal testing or leaks and have not been independently reproduced, making reliable comparisons difficult.

Agentic workflows and tool use

Modern benchmarks increasingly measure how well models coordinate tools and perform multi‑step tasks.

Google reports that Gemini 3.5 Flash leads several tool‑use evaluations, including 83.6% on MCP Atlas and 56.5% on Toolathlon, benchmarks designed to measure multi‑tool orchestration and real‑world workflows.

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

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

Context window, speed, and cost considerations

Benchmarks alone do not capture deployment characteristics.

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

Gemini 3.5 Flash is designed as a high‑speed inference model and is often described as significantly faster than frontier models while remaining competitive on several agentic benchmarks.

DeepSeek models typically focus on open‑weight or lower‑cost deployment strategies, which can make them attractive for organizations that want to run powerful models locally or on custom infrastructure.

Independent evaluation of DeepSeek V4

The most credible independent assessment of DeepSeek V4 comes from the U.S. National Institute of Standards and Technology’s CAISI program.

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

The report also notes that DeepSeek’s internal benchmark results appear stronger than the independent CAISI measurements, highlighting the importance of neutral evaluations in comparing models across labs.

Why cross‑model comparisons remain imperfect

Even with published numbers, comparing models directly remains difficult 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 indexes and Elo scores (like GDPval‑AA) are not directly comparable with percentage‑based benchmarks.

Because of these issues, a strict “1‑to‑5 ranking” across all models should be interpreted cautiously.

What the evidence suggests right now

Based on the strongest available public data:

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

In practice, the “best” model depends heavily on workload: coding agents, research assistants, long‑context analysis, and cost‑sensitive inference can all favor different models despite similar headline benchmarks.