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The Coding Assistant Breakdown: More Tokens Please

·4 mins
Table of Contents

Source: SemiAnalysis
Date: April 24, 2026
Subject: State of AI Coding Assistants and the Shift Toward Agentic Workflows

Are We Entering the Era of “Agentic” Coding? #

If you’ve been using AI to help you code lately, you’ve likely felt the shift. It’s no longer just about finishing your sentences; it’s about solving entire Jira tickets. The latest SemiAnalysis report, “The Coding Assistant Breakdown: More Tokens Please,” pulls back the curtain on the frontier models dominating our screens: GPT-5.5, Claude Opus 4.7, and DeepSeek V4.

The big takeaway? We are moving past “chatbots that code” and into a world of autonomous agents. But here’s the kicker: as developers, we seem to care less about “perfect logic” and more about inference speed and token efficiency. Why? Because nothing kills a “flow state” faster than waiting for a slow model, and “cost per task” is becoming the true north star metric.

1. Model Evaluations: The Frontier Has Moved #

GPT-5.5: OpenAI’s “Spud” Arrives #

After months of Anthropic leading the pack, OpenAI has finally returned to the frontier with GPT-5.5 (codename “Spud”).

  • Pricing: It’s not cheap—$5/M input and $30/M output tokens (2x more expensive than 5.4).
  • Priority Tier: They’ve introduced a priority tier at 2.5x the standard rate for those who need concrete SLAs (e.g., >50 tokens/sec).
  • Token Efficiency: Interestingly, GPT-5.5 scores higher on benchmarks while using fewer tokens than 5.4. This is a game-changer for “cost per task.”
  • Reasoning Levels: You can now choose between xhigh, high, medium, low, and non-reasoning effort—a direct tradeoff between cost and capability.

Claude Opus 4.7: The Quality King (With a Price Increase?) #

Anthropic released Opus 4.7 as a drop-in replacement for 4.6, but it comes with some “fine print.”

  • New Tokenizer: 4.7 uses a more granular tokenizer that can increase total token usage (and thus price) by up to 35%.
  • Visual Power: It now supports high-resolution screenshots for frontend styling, preferring visual inspection over headless browser tests.
  • The Workflow Friction: The team noted a behavioral shift: the model uses fewer tool calls and more “reasoning” by default. If you want it to actually do things, you might have to crank the reasoning to “xhigh.”
  • Recent Bugs: Anthropic recently posted a postmortem for three bugs (March-April) that affected almost all Claude Code users. “Live by the sword, die by the sword.”

DeepSeek V4: The Open-Source Disruptor #

DeepSeek continues to commoditize intelligence with V4-Pro (1.6T total / 49B active) and V4-Flash.

  • 1M Context Window: Their core advancement is long-context performance.
  • Technical Wizardry: Using techniques like Compressed Sparse Attention (CSA), they’ve achieved a 90% reduction in KV Cache compared to V3.2.
  • Day-Zero Speed: On H200 clusters, this model hits a blistering 150 tokens/sec throughput.

2. R.I.P. Traditional Benchmarks? #

Do you still trust SWE-bench scores? The report suggests we probably shouldn’t. Most “verified” benchmarks are still riddled with implementation-specific unfair tests or contamination from training data.

  • The “Expert-SWE” Shock: In a sneaky model card reveal, it turns out GPT-5.5 actually got “mogged” (significantly beaten) by Opus 4.7 on OpenAI’s own Expert-SWE benchmark.
  • GDPval (OpenAI): The new gold standard. It tests agents on “economically valuable tasks” across 44 professions using expert contractors and simulated corporate environments (emails, Slack, etc.).

3. VIBEZ: Our Hands-On Impressions #

Which model should you actually use? The SemiAnalysis team has settled on a hybrid workflow:

  1. Start with Claude: Use it for the initial plan, scaffolding, and the first “Proof of Concept” (POC). Claude is better at inferring “true intent” from terse, messy human instructions.
  2. Switch to Codex (GPT-5.5): Switch here to actually solve the bugs or fix specific issues. Codex is “smarter” at understanding complex data structures and reasoning about code structure literally.

The “Thinking” Difference:

“Codex pulls in a shit ton of more granular context from the internet + codebase and then makes a directed effort… Opus 4.7 often feels like it just does a quick explore and then #yolos changes.”

4. The Strategic Outlook: Cost Per Task #

The subtitle “More Tokens Please” refers to the massive amount of internal reasoning (Thinking tokens) agents now perform. As we move forward, we should stop looking at Price per Token and start looking at Price per Successful PR.

What do you think? Would you pay 2.5x more for a “Priority” tier if it guaranteed you stay in your flow state? Or are you sticking with the open-source power of DeepSeek?

Note: This report is a synthesized summary based on the April 25, 2026 SemiAnalysis newsletter. Direct access to the full paywalled content was used to enrich this overview.