Claude Opus 4.8 is the best available model overall in June 2026, leading on agentic coding (SWE-Bench Pro: 69.2%), computer use (OSWorld-Verified: 83.4%), and knowledge work (GDPval-AA: 1812 Elo). GPT-5.5 dominates terminal/CLI automation and long-context retrieval at 94.8% on MRCR v2. Gemini 3.5 Flash is the value king at 4x the speed and 70% lower cost, leading on MCP tool orchestration and multimodal reasoning. No single model sweeps every category — you pick based on workload.
Quick Verdict: Which Model Should You Use?
I’ve been running all three through production pipelines for the past month. Here is the short version:
- For agentic coding (multi-file refactors, PR reviews, bug hunting): Opus 4.8. It leads SWE-Bench Pro by 10.6 points over GPT-5.5 and 14.1 over Gemini 3.5 Flash. In my testing, it handles five-file refactors with fewer dropped context edges than either competitor.
- For terminal/CLI agentic workflows: GPT-5.5. It scores 78.2% on Terminal-Bench 2.1 and uses ~40% fewer output tokens than Opus 4.8 on equivalent tasks. When you are running an agent loop with 50+ tool calls, that token efficiency adds up fast.
- For cost-sensitive production at scale: Gemini 3.5 Flash. At $1.50/MTok input and $9/MTok output, it is roughly 4x cheaper than Opus 4.8 and 3x cheaper than GPT-5.5. It also outputs at ~222 tokens/sec versus Opus 4.8’s ~67 t/s.
- For long-context / RAG workflows: GPT-5.5. It scored 94.8% on MRCR v2 at 128K tokens — a 35.5-point lead over Opus 4.8’s 59.3%. If you are processing 500-page documents, this matters.
How Do These Models Compare on Benchmarks?
June 2026 is the most competitive month in AI history. All three labs shipped flagship models within six weeks. Here is how they stack up on the benchmarks that matter to working developers:
| Benchmark | Opus 4.8 | GPT-5.5 | Gemini 3.5 Flash |
|---|---|---|---|
| SWE-Bench Pro | 69.2% | 58.6% | 55.1% |
| SWE-Bench Verified | 88.6% | ~78% | N/A |
| Terminal-Bench 2.1 | 74.6% | 78.2% | 76.2% |
| OSWorld-Verified | 83.4% | 78.7% | 78.4% |
| MCP-Atlas | 82.2% | 75.3% | 83.6% |
| GDPval-AA (Elo) | 1,890 | 1,769 | 1,656 |
| HLE (no tools) | 49.8% | 41.4% | 40.2% |
| MRCR v2 (128K) | 59.3% | 94.8% | 77.3% |
| Finance Agent v2 | 53.9% | 51.8% | 57.9% |
| GPQA Diamond | 93.6% | 93.5% | N/A |
A few things jump out. First, SWE-Bench Verified is nearing saturation — all frontier models are above 78%. The real differentiator is now SWE-Bench Pro, which tests harder, multi-step software engineering tasks. Second, no model holds a monopoly. Opus 4.8 dominates coding and agentic benchmarks. GPT-5.5 destroys long-context retrieval. Gemini 3.5 Flash leads financial agent tasks and MCP tool orchestration.
What About the Intelligence Index?
The Artificial Analysis Intelligence Index gives Opus 4.8 a score of 61.4 (#1 overall), GPT-5.5 a 60.2 (#2), and Gemini 3.5 Flash a 55.3 (#10). That 6.1-point gap between Opus and Flash is roughly the difference between a model that can reliably plan and execute multi-hour coding tasks and one that needs more supervision.
But the Intelligence Index does not account for speed or cost. Gemini 3.5 Flash is 4x faster than Opus 4.8. When you are running 10,000 inference requests per minute, that throughput gap changes the equation entirely.
Which Model Is Best for Coding?
Opus 4.8, by a meaningful margin.
I migrated a production Next.js app from App Router to Pages Router last week. Opus 4.8 handled the full migration across 14 files in one pass with zero regressions. GPT-5.5 needed two follow-up corrections on routing logic. Gemini 3.5 Flash handled individual file migrations fine but struggled with the cross-file dependency chain.
The benchmark data backs this up. On SWE-Bench Pro, Opus 4.8 scores 69.2% versus GPT-5.5’s 58.6% — a 10.6-point gap. On LiveCodeBench, Opus 4.8 hits 88.8%. On OSWorld-Verified (computer use / browser automation), Opus 4.8 scores 83.4% versus GPT-5.5’s 78.7%.
Opus 4.8 also has the best “code honesty” I’ve seen. Anthropic claims a 4x improvement in proactively flagging issues. In practice, Opus 4.8 tells me when a proposed approach has edge cases or performance problems instead of just generating the code and hoping it works.
For earlier generation comparisons, see my post on Claude Mythos vs GPT-5.4 vs Gemini 3.1 Pro, which covered the previous frontier landscape.
Which Model Is Best for Terminal / CLI Work?
GPT-5.5 wins this category.
OpenAI’s GPT-5.5 scores 78.2% on Terminal-Bench 2.1, ahead of Opus 4.8’s 74.6% and Gemini 3.5 Flash’s 76.2%. In my testing, GPT-5.5 is notably better at parsing terminal output, understanding error messages, and generating correct shell commands on the first try.
It also uses fewer tokens. GPT-5.5 produces roughly 40% fewer output tokens than GPT-5.4 on equivalent tasks, and it is noticeably more concise than Opus 4.8. When you are running an agent loop with 100+ tool calls, that difference translates to real dollars.
The catch? GPT-5.5 has a long-context surcharge: prompts above 272K tokens are billed at 2x input and 1.5x output for the full session. If you habitually dump entire repositories into context, that surcharge will hurt.
Which Model Offers the Best Value?
Gemini 3.5 Flash, and it is not close.
| Pricing (per 1M tokens) | Opus 4.8 | GPT-5.5 | Gemini 3.5 Flash |
|---|---|---|---|
| Input | $5.00 | $5.00 | $1.50 |
| Output | $25.00 | $30.00 | $9.00 |
| Cached input | $0.50 | $0.50 | $0.15 |
| Batch discount | 50% | 50% | N/A |
Gemini 3.5 Flash is 70% cheaper on input and 64% cheaper on output than Opus 4.8. Its cached input rate of $0.15/MTok is ideal for RAG pipelines or long-context workflows where you repeatedly process the same corpus.
The trade-off is raw quality. Gemini 3.5 Flash scores 55.3 on the Intelligence Index versus Opus 4.8’s 61.4. In practice, I would not trust Gemini 3.5 Flash to autonomously refactor a production codebase without human review. One analysis found a ~61% hallucination rate on complex tasks — fine for summarization, risky for unattended code generation.
For a deeper look at cost optimization trade-offs across the model landscape, check out my Claude Fable 5 vs Opus 4.8 vs DeepSeek V4 Pro comparison.
Which Model Is Best for Long-Context and RAG?
GPT-5.5 wins decisively.
On MRCR v2 at 128K tokens, GPT-5.5 scores 94.8% — a 35.5-point lead over Opus 4.8’s 59.3% and a 17.5-point lead over Gemini 3.5 Flash’s 77.3%. If your workload involves retrieving specific facts from 500-page legal documents or codebases, GPT-5.5 is the clear choice.
Opus 4.8’s weakness on long-context retrieval surprised me. It scored 59.3% on the same benchmark — barely above chance for many tasks. This is not a context window limitation (Opus 4.8 supports 200K tokens). It is a retrieval quality issue. The model seems to lose precision past ~60K tokens.
All three models now support 1M+ token context windows (Opus 4.8 is the exception at 200K, though Anthropic may expand it). But practical retrieval quality varies hugely. Do not assume that because a model supports 1M tokens it can reason across 1M tokens.
Which Model Is Best for Multimodal Workloads?
Gemini 3.5 Flash.
It leads CharXiv Reasoning at 84.2% and MMMU-Pro at 83.6%, ahead of GPT-5.5 on both. Gemini 3.5 Flash also supports native audio and video input, whereas Opus 4.8 is limited to text, image, and code.
For chart understanding, diagram parsing, and document layout analysis, Gemini 3.5 Flash is the best choice. It also integrates natively with Google Search, Workspace, and Cloud services, which matters if you are building agents that need real-time web data.
FAQ
Is Claude Opus 4.8 better than GPT-5.5 for coding?
Yes, for agentic coding (multi-file refactors, PRs, complex bug fixes). Opus 4.8 leads SWE-Bench Pro (69.2% vs 58.6%) and OSWorld-Verified (83.4% vs 78.7%). GPT-5.5 is better for terminal/CLI automation and single-edit bug fixes where token efficiency matters.
Is Gemini 3.5 Flash good enough for production coding?
For cost-sensitive pipelines that include human review, yes. For unattended production code generation, no. Gemini 3.5 Flash scores 55.1% on SWE-Bench Pro versus Opus 4.8’s 69.2%, and independent analysis found ~61% hallucination rates on complex tasks. Use it for summarization, classification, and high-throughput pipelines.
Which model has the longest context window?
GPT-5.5 and Gemini 3.5 Flash both support 1M tokens. Opus 4.8 supports 200K. However, GPT-5.5 has the best practical long-context retrieval (94.8% on MRCR v2 at 128K), while Opus 4.8 struggles past ~60K tokens (59.3% on the same benchmark).
Should I wait for Gemini 3.5 Pro or GPT-5.6?
Possibly. Gemini 3.5 Pro is expected before end of June 2026 and may reshape the comparison at the high end. GPT-5.6 has an 89% probability on Polymarket for a mid-2026 release. If you can wait 2-4 weeks, the landscape may shift again.
What happened to Claude Fable 5?
Claude Fable 5 launched on June 9, 2026 but was shut down globally after 72 hours due to a US Commerce Department export ban on foreign nationals. It had higher benchmarks than Opus 4.8 but is effectively unavailable. Opus 4.8 is the best Anthropic model you can actually use today.
For more context on how the model landscape evolved over the past year, see my earlier comparison of GPT-6 vs Claude Opus 4.7 vs Gemini 3.1.
The Bottom Line
Pick one model per workload. Do not try to crown a single winner.
| Workload | Pick | Why |
|---|---|---|
| Agentic coding / multi-file refactors | Opus 4.8 | SWE-Bench Pro: 69.2%, best computer use |
| Terminal / CLI automation | GPT-5.5 | Terminal-Bench: 78.2%, 40% fewer tokens |
| Cost-sensitive production | Gemini 3.5 Flash | $1.50/$9 per MTok, 222 t/s throughput |
| Long-context RAG | GPT-5.5 | MRCR v2: 94.8% at 128K tokens |
| Multimodal / chart analysis | Gemini 3.5 Flash | CharXiv: 84.2%, native audio/video |
| High-stakes knowledge work | Opus 4.8 | GDPval-AA: 1,890 Elo, lowest hallucination |
If I had to pick one model as a daily driver today, I would choose Opus 4.8 for its coding and reasoning strengths. But I route terminal tasks to GPT-5.5 and high-throughput batch jobs to Gemini 3.5 Flash. Multi-model routing is not a luxury anymore — it is the practical reality of the June 2026 AI landscape.