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Gemini 3.5 Flash: Cheapest Coding Model Per Task?

Gemini 3.5 Flash lists at $1.50/$9.00 per million tokens: about 2x Haiku per token, yet roughly 3x cheaper per task than GPT-5.5 on agentic coding work.

By Capital & Compute

Gemini 3.5 Flash is the cheapest frontier-capable coding model per completed task, a different ranking than the headline-capability order in the 2026 AI coding agent landscape. Measured by cost per benchmark point on SWE-Bench Pro, Flash runs $0.163 per solved point: more than Claude Haiku 4.5 at $0.127, but Haiku scores from a weaker baseline a capability tier below, and Flash lands at roughly a third of GPT-5.5’s $0.512. Among models that clear the frontier coding bar, Flash wins on cost by a wide margin.

Cost per benchmark point: Flash, Haiku, GPT-5.5Bar chart showing cost per solved benchmark point on SWE-Bench Pro. Flash at $0.163, Haiku at $0.127, GPT-5.5 at $0.512. Flash is the middle option on absolute cost but delivers frontier capability.$0.000$0.200$0.400$0.600Flash$0.163Haiku$0.127GPT-5.5$0.512
Cost per benchmark point: Flash, Haiku, GPT-5.5
ItemValue
Flash$0.163
Haiku$0.127
GPT-5.5$0.512
Cost per benchmark point solved: Gemini 3.5 Flash vs Claude Haiku 4.5 vs GPT-5.5 on SWE-Bench Pro. Flash is 3x cheaper than GPT-5.5 and only slightly more expensive than Haiku, despite Haiku's lower baseline capability.Source: Derived from published API rates and SWE-Bench Pro benchmark scores

For live coding tasks with real loop assumptions, Flash runs roughly $0.02-$0.03 per task on simple fixes and $0.13-$0.18 on multi-step agentic work, making it the cheapest option that clears the “production-viable” coding bar.

The Price Frame

Google launched Gemini 3.5 Flash on May 19, 2026, at Google I/O. The pricing is straightforward:

  • Input: $1.50 per million tokens
  • Output: $9.00 per million tokens

For comparison:

  • Claude Haiku 4.5: $1.00 / $5.00 per million tokens
  • GPT-5.5: $5.00 / $30.00 per million tokens (standard tier)

Flash costs 1.8x what Haiku does per output token ($9 versus $5), but output is what drives real cost variance in agent loops. The question is whether Flash’s stronger coding chops justify the higher per-token rate.

Coding Benchmarks: Where Flash Stands

Reported benchmark results, compiled by third-party trackers LLM Stats and DataCamp and not independently verified here, put Gemini 3.5 Flash at:

  • Terminal-Bench 2.1: 76.2% (GPT-5.5 leads at 78.2%; Claude Opus 4.7 at 66.1%)
  • SWE-Bench Pro (Public): 55.1% (GPT-5.5: 58.6%; Claude Opus 4.7: 64.3%)
  • SWE-Bench Verified: 74.4% (marginal edge over Gemini 3.1 Pro’s legacy 78%)
  • MCP Atlas: 83.6% (agentic tooling; GPT-5.5: 75.3%)
  • Finance Agent v2: 57.9% (beats GPT-5.5’s 51.8% on structured data and charts)

Flash lands squarely between Claude Haiku 4.5 (>73% SWE-Bench Verified) and GPT-5.5 on traditional coding but wins decisively on agentic multi-step tool use. For pure terminal automation and shell scripting, GPT-5.5 pulls ahead. For tool-calling workflows and structured reasoning, Flash is the strongest in its price tier.

Cost-Per-Task: Real Loop Math

Using realistic loop assumptions from live deployments (plug your own token volumes into the AI coding cost calculator to see where Flash lands for a specific workload):

Simple fixes (one-shot or minimal looping), 8K input plus 1.2K output tokens:

  • Flash: $0.023 (8K x $1.50/M plus 1.2K x $9/M)
  • Haiku: $0.014 (8K x $1/M plus 1.2K x $5/M)
  • GPT-5.5: $0.076 (8K x $5/M plus 1.2K x $30/M)

Multi-step agentic (5-step loops, tool calling), 40K input plus 8K output tokens:

  • Flash: $0.132 (40K x $1.50/M plus 8K x $9/M)
  • Haiku: $0.080 (40K x $1/M plus 8K x $5/M)
  • GPT-5.5: $0.440 (40K x $5/M plus 8K x $30/M)

Cost per benchmark point (SWE-Bench Pro output price divided by score solved):

  • Flash: $0.163 ($9/M output divided by 55.1% solved)
  • Haiku: $0.127 ($5/M output divided by 39.45% solved, a weaker baseline)
  • GPT-5.5: $0.512 ($30/M output divided by 58.6% solved)

The math reveals the crux: Flash’s higher per-token rate is offset by needing fewer tokens to solve harder problems. On multi-step agentic work where looping becomes expensive, Flash’s lower output cost relative to GPT-5.5 compounds into more than 3x cost savings per completed task ($0.132 versus $0.440). Against Haiku, Flash costs roughly 1.6x more per task but solves about 40% more problems (55.1% versus 39.45% on SWE-Bench Pro), making it the choice when you are hitting Haiku’s capability ceiling.

When Flash Wins; When It Doesn’t

Pick Gemini 3.5 Flash when:

  • Building tool-heavy agent workflows or multi-step agentic loops (MCP Atlas performance is decisive here)
  • Cost per completed task is a constraint (beats GPT-5.5 on price-to-capability)
  • Processing financial data, charts, or structured reasoning (Finance Agent v2 strength)
  • Running high-volume inference where output tokens dominate the bill

Stick with Claude Haiku 4.5 when:

  • Tasks are simple and single-shot (Haiku runs about 40% cheaper per simple task and is fast enough)
  • Your codebase is already instrumented for Anthropic’s Batch API or prompt caching (the 50% discount layers on the base price)
  • SWE-Bench Pro ceiling is acceptable (Haiku at 39.45% is good for junior-level fixes and refactoring)

Use GPT-5.5 only when:

  • Terminal-heavy DevOps or shell orchestration is primary (78.2% on Terminal-Bench 2.1 vs Flash’s 76.2%)
  • Abstract reasoning or long-context fidelity past 128K tokens is non-negotiable
  • Your team is already deep in the OpenAI ecosystem

The Real Metric: Cost Per Solved Task

Scaling a coding agent to 10,000 tasks per month with 60% success rate on Haiku costs roughly $700/month. On Flash at the same success rate, the cost rises to $1,400/month. But Flash solves 40-50% harder problems, so the effective cost per solved task actually falls below Haiku’s on real workloads that encounter non-trivial fixes. Multiply that across 10,000 tasks and Flash becomes cheaper when your task distribution skews toward multi-file refactoring and tool-calling workflows.

Batch processing both models adds a 50% discount. Flash with Batch API reaches $0.06-$0.09 per multi-step task, cementing it as the cheapest frontier option for agent scale.

Why This Matters

Gemini 3.5 Flash shipped at Google I/O as a direct challenge to Claude Haiku’s cost-per-token position. Flash is not cheaper per token; it is cheaper per task and cheaper per benchmark point. That per-token-versus-per-task gap is the same lens behind the price-reversal phenomenon, where a cheaper-per-token model can cost more to finish the job. That distinction matters for anyone evaluating models on what you actually pay: cost to deliver a working code change. At May 2026 pricing, Flash closes the gap with GPT-5.5 on coding benchmarks while cutting the price in half, repositioning the cost-per-task ladder in Google’s favor for the first time since Haiku launched.

The move commodifies coding models at the frontier level. If Flash holds these pricing and performance numbers through the end of 2026, it will force Anthropic and OpenAI to compete harder on cost-per-outcome rather than pure capability.

Sources

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